Home

SNePS 2.7.1 User`s Manual - University at Buffalo, Computer

1. OBJECT1 M23 ACTION SAY OBJECT1 Come here OBJECT2 M17 ACTION SAY OBJECT1 BILL About to do M23 ACTION SAY OBJECT1 Come here I wonder if the act M23 ACTION SAY OBJECT1 Come here has any preconditions The act M23 ACTION SAY OBJECT1 Come here has no preconditions Come here About to do M17 ACTION SAY OBJECT1 BILL I wonder if the act M17 ACTION SAY OBJECT1 BILL has any preconditions The act M17 ACTION SAY OBJECT1 BILL has no preconditions BILL Now doing DO ALL M38 ACTION BELIEVE OBJECT1 M25 AGENT BILL LOCATION HERE STATE 15 Believe M25 AGENT BILL LOCATION HERE STATE IS CPU time 2 07 4 6 THE EXECUTION CYCLE PRECONDITIONS AND EFFECTS 45 e The current version of SNeRE will believe that all effects of an act are achieved even though this may be a naive assumption e The current version of the primitive action function do all is define primaction do all objectl do ns act objectl schedule act act but the user could redefine it if she wanted to make a more intelligent decision about the order in which the acts should be performed e The current version of the primitive action function do one is define primaction do one objectl schedule act lisp list to ns if snip choose
2. x describe deduce agent who state is location here M6 AGENT Stu LOCATION HERE STATE IS M6 CPU time 0 17 perform build action snif objectl build condition build agent Bill state is location here then build action say objectl Hello object2 Bill build condition build agent Stu state is location here then build action say object1l Hello object2 Stu build else build action say objectl No one s object2 here Hello Stu CPU time 0 50 x perform build action disbelieve objectl M6 CPU time 0 02 perform build action snif objectl build condition build agent Bill state is location here then build action say objectl Hello object2 Bill build condition build agent Stu state is location here then build action say object1l Hello object2 Stu build else build action say objectl No one s object2 here No one s here CPU time 0 27 32 CHAPTER 4 SNERE THE SNEPS RATIONAL ENGINE sniterate object1 where objecti is a set of guarded acts If at least one of the guard s conditions is asserted sniterate performs the act of a random one of the guards whose condition is asserted and then performs the entire sniterate again When none of the guards has an asserted condition if there is an el seact itis performed and the sniterate terminates x describe deduce agent
3. 9 5 2 Interacting with SNePS The GATN in this section accepts the same fragment of English as the one in the previous section but instead of building and returning parse trees it builds a SNePS network representing the information in the statements and answers the questions The statements are echoed and the questions are answered in English generated by the generation part of this GATN i7 First the SNePS relations used in the GATN are defined define agent act object propername member class lex 77 Next a global variable a global constant and two functions are defined defvar SaynBeforeVowelsx nil If true and the next word starts with a vowel print n before that next word defconstant x vowelsx Ha i Ho u A list of the vowels i7 7 The following two functions implement a phonological component 77 that can be used to output words and phrases from arcs of the GATN 77 In this way the beginning of the sentence can be uttered befor jj the rest of the sentence has been composed defun SayOneWord word Prints the single WORD which must be a string or a node If the word is a sets SaynBeforeVowels x If x SaynBeforeVowelsx is set then prints n before word s if the first letter of word s is a vowel check type word or string sneps node when sneps node p word setf word format nil 7A word when SaynBeforeVowelsx when member ch
4. BASE NOD E VA ROGN pro SNEPS WIT HANS OTTO M3 M4 gn g H SNI F PSs SN SNEPS SNEPS SN M5 M6 EPSULI CHAPTER 5 PROGRAM INTERFACE M7 OTTO OTTO he definition of myassert looks like ZE ELATION SNEPS W SNE E EST E PSULI h ZE RE h ES SNE SNEPS W EVAL EPSUL SCRIB SNEE re PS SNEP PSULIZE SNIP ATION EST F BAS E NOD SULIZ S ETO NOD SNEPS PSNEVAL NIL SNEPS WI SNEPS SNEP H SNEPSUL EVAL ASSERT RELATION S y SN SNEPS OPSNEVAL PS SNE S B CDR NODI NO ry S SNEPSUL R ELATION E VAR MYREL PSULIZ BAS E NOD NIL SNEPS W Aa Bf H SNEPSUL EVAL T h ASSERT S NEPS ARG ILD S N YREL SN EPSUL HANS SNEP EPSUL SULIZE R YREL E SNEPS EVAL NIL EPSUL EVAL SCRIBE F SNE EVAL NIL ELATION S
5. o e e 67 0 3 2 Restrictions 6 0 0 tie oh eA oe ea ee RE bad ee PS 68 6 6 The Tell Ask Interface e 69 6 7 TheJavaSNePS APP ooo a OG A Se Se ae en Sa 70 Procedural Attachment 73 SNeBR The SNePS Belief Revision System 75 8 1 Hypotheses Contexts and Belief Spaces o a 75 8 2 Responsibilities of SNeBR ee 75 8 3 Recognizing a Contradiction 0 20 0 000000 eee ee 75 8 4 Identifying Possible Culprits e 76 8 5 Choosing a Culprit s see os dos e e Sth a a ad ay ke is 76 8 5 1 Automatic Culprit Choosing e 76 8 5 2 Assisted Culprit Choosing e 76 8 6 Disbelief Propagation 0 0 0 000 2 ee 77 8 7 Warning About a Belief Space Known to be Contradictory 0 77 SNaLPS The SNePS Natural Language Processing System 79 9 1 Top Level SNaLPS Functions 0 0 0 0 00000000002 2007 79 9 2 The Top Level SNaLPS Loop e 80 9 2 1 Input tothe SNaLPS Loop e 80 9 2 2 SNAaEBS Variables a ae Ga rt Ded eee Bee BR 80 9 3 Syntax and Semantics of GATN Grammars 220000005 81 ES LE GATS oe es seers i Seve tte Dee Ss ak cig a tee chy oA Gp ety hc Weenies eld o aE we Sete 82 O73 2 ACUONS ua a A A Dey oth Bud a RAMS 83 9 3 3 Pfeactions dia eae GPa A ee ee Ae BO ee ee Ee 84 0 34 TerminaliActlons acc a oe iS Bet wR A Oa Re 84 IN AN 84
6. remove from context SNePSLOGsymbol pTermSet Removes the terms that match the patterns in pTermSet from the context named SNePSLOGsymbol set context SNePSLOGsymbol pTermSet Defines a context named SNePSLOGsymbol and sets its initial set of hypotheses to be the terms that match the patterns in pTermSet Note that pTermSet could be empty or omitted in which case the new context is initialized with an empty set of hypotheses set default context SNePSLOGsymbol Makes the context named SNePSLOGsymbol the current default context set mode 1 The knowledge base is cleared and SNePSLOG is put into Mode 1 the default mode In this mode every term of the form P x Xn is represented by a node of the form Ur P at x1 o an Xn Mode 1 is the mode to use when there is no specific reason to use Mode 2 or Mode 3 It requires less set up effort for the user than Mode 3 because no frames need to be defined Inference in Mode is less efficient than in the other modes because more terms match any given pattern set mode 2 The knowledge base is cleared and SNePSLOG is put into Mode 2 In this mode every proposition of the form P x1 xn is represented by a node of the form lt rel P P gt lt rel arg P1 xl gt lt rel arg Pn xn gt Mode 2 is a compromise between Modes 1 and 3 It requires no more user set up effort than Mode 1 does inference is more efficient than in Mode 1 because f
7. 6 21 2002 9 51 59 wot gt atnin grammar lisp State S processed State S1 processed State VP processed State VP V processed State S OBJ processed State NP processed State NP ART processed State NP END1 processed State NP END2 processed State S END1 processed State S END2 processed Atnin read in states S END2 S END1 NP END2 NP END1 NP ART NP S OBJ VP V VP 7 EI S1 S gt lexin lexicon lisp undefined NIL a phor some dog man men bite bites like likes gt parse ATN parser initialization Trace level 0 Beginning at state S Input sentences in normal English orthographic convention Sentences may go beyond a line by having a space followed by a lt CR gt To exit the parser write end A dog bit John Resulting parse S MOOD DECL NP DEFINITE NIL N dog VP V bite NP DEFINITE T NPR John Time sec 0 05 The dog slept Resulting parse S MOOD DECL NP DEFINITE T N dog VP V sleep Time sec 0 05 Mary believes that John likes the dog Resulting parse 9 5 EXAMPLES 93 S MOOD DECL NP VP Time DEFINITE T NPR Mary V believe NP DEFINITE T S MOOD DECL NP DEFINITE T NPR John VP V like NP DEFINITE T N dog sec 0 117
8. maT ctgy art definite nil the ctgy art definite t Computer Science ctgy npr John ctgy npr Mary ctgy npr computer ctgy n Computer ctgy multi start multi rest Science ctgy n root computer dog ctgy n man ctgy n plur men men ctgy n root man num plur woman ctgy n plur women women ctgy n root woman num plur saw ctgy n ctgy v root see tense past believe ctgy v stative t bit ctgy v root bite tense past bite ctgy v num plur past bit hike ctgy v num plur see ctgy v past saw sleep ctgy v past slept slept ctgy v root sleep tense past study ctgy v use ctgy v who ctgy wh what ctgy wh and here is a test run The output has been edited by changing some line breaks and some indentation in order to show the parse trees more clearly USER 29 sneps Welcome to SNePS 2 5 PL 1 1999 08 19 16 38 25 92 CHAPTER 9 SNALPS THE SNEPS NATURAL LANGUAGE PROCESSING SYSTEM Copyright C 1984 1999 by Research Foundation of State University of New York SNePS comes with ABSOLUTELY NO WARRANTY Type copyright for detailed copyright information Type demo for a list of example applications
9. O36 TESS O teh oe be nab la er ea tute Leis ey tek eet Be bat 85 CONTENTS y 9 3 7 Terminal Symbols eee e ee A 85 9 4 Morphological Analysis and Synthesis o e 86 94 1 Syntax of Lexicon Files encre sida re a a a dr de ak 86 9 4 2 Functions for Morphological Analysis o o e 87 9 4 3 Functions for Morphological Synthesis o o o 88 95 Example ici E a A A We eS ia e ak 89 9 5 1 Producing Parse Trees pn sarne sd Be ee Bd a See Re A ee eo 89 9 5 2 Interacting withSNePS 2 2 00 0 0 00000000000 95 10 SNePS as a Database Management System 101 10 1 SNePS as a Relational Database o e 101 10 131 Projectar ica a da A a E de ol 101 10 1 2 Select eig ri A ee RA o eR ee e e 104 LOB JOM rr a e A e at IDA ds 104 10 2 SNePS as a Network Database o e 105 10 3 Database Functions cc e rag pe e a es 106 University at Buffalo Public License UBPL Version 1 0 107 ie ADenmnitionss so ssi es echo eee wey Peak Gee Peony ven Ace dee ly ebook Rt etude Pe A 107 2 Source Code License ic dt doe eae ee eed Gece koe ke 108 2 1 The Initial DeveloperGrant ee 108 2 2 Contributor Grant ss ss e cece ee n a 109 3 Distribution Obligations aoaaa a 109 3 1 Application of License a a a E e e e a e 109 3 2 Availability of Source Code o e sacs ee 109 3 3 Descript
10. setr mood decl liftr mood The state RESPOND must know whether it is echoing a statement or answering a question to vp cat vt Accept just a simple verb for this example setr act to vp v and ignore tense vp v push np t sendr mood setr object Set OBJECT to parse of object to s final jump s final t If no object s final jump s end overlap embedded t an embedded proposition wrd overlap mood decl to s end wrd overlap mood question to s end s end pop assert agent getr agent Assert a top level statement act build lex getr act object getr object and overlap mood decl nullr embedded pop 2 build agent getr agent Build an embedded statement act build lex getr act T F r r r E r r object getr object and getr embedded overlap mood decl pop deduce agent getr agent Use deduce to answer a question act build lex getr act object getr object overlap mood question Notice in all three above arcs that if there is no object getr object will evaluate to NIL and the node will be built without an OBJECT arc 9 5 EXAMPLES 97 np wrd that t to nomprop an embedded proposition cat npr t setr head or 7 First try to find someone with the given name find compose object propername getr Otherwise cr
11. defsnepscomis available in all standard SNePS packages Hence it can normally always be used with out a package qualifier If it is used in a non standard package it should be written as sneps defsnepscom undefsnepscoms commands Undefines all commands The function definitions of the individual commands will not be removed but the listed commands will not be available as SNePSUL commands anymore For example the following will undo the inappropriate definition of Example 3 above x undefsnepscoms lex build T CPU time 0 00 lex build Lucy SNePS ERROR Invalid top SNePSUL form LEX BUILD LUCY Occurred in module TOP EVALUATOR in function TOPSNEVAL Do you want to debug it n 54 CHAPTER 5 PROGRAM INTERFACE Chapter 6 SNePSLOG 6 1 SNePSLOG Basics SNePSLOG is a logic programming interface to SNePS That is almost everything that can be done interac tively using SNePSUL can be done interactively using SNePSLOG just with a syntax that looks more like traditional symbolic logic than SNePSUL does Use of SNePSLOG rather than SNePSUL is recommended for the SNePS novice To enter SNePSLOG load SNePS and evaluate snepslog To leave SNePSLOG execute the SNePSLOG command lisp The default Common Lisp package for symbols read by the SNePSLOG reader is snepslog The full details of the SNePSLOG syntax is in 6 2 The semantics are given in 6 3 6 2 SNePSLOG Syntax The SNePSLOG syntax
12. nl tell 70 node to lisp object 47 nodes asserted 6 7 base 6 in contexts 7 molecular 6 pattern 6 proposition 7 types of 6 unasserted 6 7 variable 6 61 nodes SNEPSUL variable 7 nogood 77 normal 60 not 14 ns to lisp list 47 nscommands SNEPSUL variable 7 or 14 outnet 10 parse 79 parser nl tell 70 path based inference 13 paths syntax and semantics 13 patterns SNEPSUL variable 7 perform 27 40 60 plan act 36 plan goal 39 plantracex 40 Precondition 68 primitive action functions 28 table of 36 primitive actions 28 primitive acts 28 procedural attachment 73 procedure SNEPSUL 5 range restrict 15 reduction inference 12 13 relations 12 converse 12 initial 12 INDEX relations SNEPSUL variable 7 12 relative complement 14 remove from context 15 60 resetnet 17 rscommands SNEPSUL variable 7 set context 15 60 set default context 15 60 set mode 1 60 set mode 2 60 set mode 3 60 show 2 3 5 18 61 silent erase 16 SNeBR 75 sneps 9 snepslog ask 70 snepslog init java sneps connection 70 snepslog tell 69 SNeRE 27 67 snif 31 67 SNIP 21 64 sniterate 31 67 snsequence 30 67 support set 61 support set 59 76 surface 19 tell 69 70 terminalPunctuation 61 62 topcommands SNEPSUL variable 7 trace 61 unadopt 30 67 unbreak arc 80 undefine 12 undefine path 13 61 undefsnepsc
13. null geta object No object 98 CHAPTER 9 SNALPS THE SNEPS NATURAL LANGUAGE PROCESSING SYSTEM g end pop nil t gnp to gnp end geta propername geta object say geta propername geta object Generate an npr to gnp end geta class geta member An indef np say cons a find lex class member getr call g x geta act say that An embedded proposition to gnp end gnp end pop nil t Here is a sample run using this grammar and the same lexicon as before gt parse ATN parser initialization Trace level 0 Beginning at state S Input sentences in normal Sentences may go beyond a To exit the parser write A dog bit John English orthographic convention line by having a space followed by a lt CR gt end I understand that a dog bit John Time sec 0 25 The dog slept I understand that a dog slept Time sec 1 884 Mary believes that John likes the dog I understand that Mary believed that John liked a dog Time sec 0 4 Mary studies Computer Science T understand that Mary studied Computer Science Time sec 0 2 Mary used a computer I understand that Mary used a computer Time sec 0 233 John saw a saw I understand that John saw a saw Time sec 0 217 What bit John a dog bit John Time sec 0 167 9 5 EXAMPLES Who sleeps a dog slept Time sec 0 917 Who studied
14. 24 CHAPTER 3 SNIP THE SNEPS INFERENCE PACKAGE The Numerical Quantifier pat a1 2n Ri 1 Rn an P e1 2n means that of the k substitutions o t x tn amp n for which the following conditions hold e t satisfies the restriction R 1 lt i lt n e t At whenever i j e t does not already occur in the rule 1 lt i lt n between and j of them also make P x1 t o true There may be fewer restrictions than variables if some restriction contains more than one variable free as long as every variable occurs in at least one restriction A numerically quantified rule is built with the SNePSUL command assert emin emax j etot k pevb 1 n amp ant R z Dis Ra 2 cq P x tn The first occurrence of a variable must be preceded by the macro and subsequent occurrences must be preceded by the macro The Uniqueness Principle for Variables Currently the Uniqueness Principle that every entity represented in the network is represented by a unique node is not enforced by SNePS for variables Therefore it is advised that the Uniqueness Principle for variables be followed by the SNePSUL user as a matter of style This should be done as follows Every restriction R used in a restricted quantifier should have a series of variables x x Every rule that uses R once should use z as its variable A rule that uses the restriction R more than once should use x in the first use o
15. Connectives 3 1 2 Quantifiers and Chapter 4 SNeRE undefine frelationy Undefines each relation If any relation is being used in the current network the arcs are not removed from the network structure but they do become undefined undefine is most useful in correcting typographical errors in calls to define 2 5 1 Reduction Inference An asserted node with a certain set of arcs emanating from it implies another node with a subset of those arcs Using this implication to derive new nodes is called reduction inference and is implemented and used by deduce For example x describe assert member snoopy rover class dog animal M1 CLASS ANIMAL DOG MEMBER ROVER SNOOPY M1 CPU time 0 08 x describe deduce member snoopy class dog 2 5 RELATIONS 13 M2 CLASS DOG MEMBER SNOOPY M2 CPU time 0 05 describe assert agent john act gives object book 1 recipient mary M3 ACT GIVES AGENT JOHN OBJECT BOOK 1 RECIPIENT MARY M3 CPU time 0 08 x describe deduce agent john act gives object book 1 M4 ACT GIVES AGENT JOHN OBJECT BOOK 1 M4 CPU time 0 05 Warning According to Shapiro 1991 if you build a node that is implied via reduction inference by an already asserted node the new node will automatically be asserted This is not implemented in the current version of S
16. If none of the pi can be inferred and if else da is included perform da Otherwise do nothing sniterate if pl al if pn an else da Using deduce determine which of the pi hold If any do nondeterministically choose one of them say p3 perform aj and then perform the entire sniterate again If none of the pi can be inferred and if else da is included perform da Otherwise do nothing snsequence al a2 Perform al and then perform a2 withall x p x a x da Using deduce determine which if any entities satisfy the open proposition p x If any do say el en perform a ei on each of them in a nondeterministic order If no entity satisfies p x perform da Note the question mark must appear to identify the open variable and the default act da must be included 68 CHAPTER 6 SNEPSLOG withsome x p x a x da Using deduce determine which if any entities satisfy the open proposition p x If any do nondeterministically choose one say e and perform a e If no entity satisfies p x perform da Note the question mark must appear to identify the open variable and the default act da must be included Propositions about Acts ActPlan al a2 The way to perform the act al is to perform the act a2 Typically a1 will be a simple but non primitive act and a2 will be an act structured using the control acts listed above Effect a p The effect of performing the act a is th
17. Po If 2 1 and 7 is omitted the thresh is a generalization of equivalence Use An asserted thresh may be used in forward or backward inference to conclude that one or more of its arguments is to be asserted or that the negation of one or more of its arguments is to be asserted 3 1 REPRESENTING AND USING RULES 23 3 1 2 Quantifiers Quantifiers permit the use of variables in deduction rules The relations forall and exists are predefined quantifier relations They are used to point to variable nodes indicating for which values of the variable node the rule holds forall and exists represent universal and existential quantifiers respectively SNePS 2 uses restricted quantification which means that every quantified expression must have a restriction as well as a scope The Universal Quantifier V a1 n Ri r1 Ra an Pi ai n Pm 1 2 n means that for every substi tution o t1 21 tn 2 for which the following conditions hold e t satisfies the restriction R 1 lt i lt n e t At whenever i j e t does not already occur in the rule 1 lt i lt n P 1 n o 1 lt i lt m is true There may be fewer restrictions than variables if some restriction contains more than one variable free as long as every variable occurs in at least one restriction A universally quantified rule is built with the SNePSUL command assert forall 11 n amp ant R z1 LEE Rn n cgi
18. SNEPSUL HANS SNEPSUL FRANZ SNEPSUL OTTO SNIP TEST had to replace the s with s here comment problem SNEPS SNEPSUL MYBASE Combination of and 7 SNEPS DESCRIBE EPSUL FRANZ SNEPSUL OTTO SNEPS ASSERT USER RELREL USER FRANZ USER OTTO SNEPSUL MYREL SNEPS x SNEPSUL MYBASE SNEPS ASSERT SNEPS ARG SNEPS BUILD SNEPSUL MYREL SNEPSUL HANS SNEPSUL RELREL SNEPSUL FRANZ SNEPSUL MYREL SNEPS SNEPSUL MYBASBE SNEPS DESCRIBE SNEPS x SNEPS NODES 7 wn Z x U wn T T 7 Now actually run it gt myassert relrel hans franz otto FRANZ OTTO B1 FRANZ 77 user franz FRANZ 7 snepsul franz HANS M1 RELREL FRANZ HANS OTTO 50 A SN 7 gt L 2 ve 1 FRANZ 3 4 HANS R M3 5 ry 1 FRANZ 6 Bl L Bl M DHMH W Py 1 FRANZ 7 A B1 FRANZ F 3 MY RANZ HAN OTTO ELR EL FRANZ OTTO REL B1 S M1 M2 EPS D F FAU T T DEFAU LTCT Here ppdef AMBDA what t myassert RELATION N S i BLOCK LET P MYASSERT O a ES
19. and B1 respectively o ee ee 37 Graphical version of the example GATN 00 0 0 00 000 90 SNePS network after running the example o o e 100 Fragment of SNePS network for the Supplier Part Project Database 102 vii viii LIST OF FIGURES Chapter 1 Introduction 1 1 General SNePS is a logic frame and network based knowledge representation reasoning and acting system The name SNePS originally was an acronym of The Semantic Network Processing System A semantic network roughly speaking is a labeled directed graph in which nodes represent entities arc labels represent binary relations and an arc labeled R going from node n to node m represents the fact that the entity represented by n bears the relation represented by R to the entity represented by m The set of nodes in the network based view of SNePS is coextensive with the set of terms in the logic based view of SNePS SNePS is called a propositional semantic network because every proposition represented in the network is represented by a node not by an arc Relations represented by arcs may be thought of as part of the syntactic structure of the node they emanate from In the frame based view of SNePS every non atomic term is a frame whose slots are the arcs of the network based view and the fillers of the slots are the nodes those arcs point to Whenever information is added to the network it is added in
20. and allegro version gt version gt 6 0 sneps adjust for acl6 before Insert the following code at the end of the main source file 77 adjustment for ACL6 and allegro version gt version gt 6 0 sneps adjust for acl6 after 2 5 Relations By relation in this manual we mean any relation used to label network arcs Therefore relation and arc label are used interchangeably Whenever an arc labelled R goes from node zx to node y SNePS considers an arc labelled R to go from y to x Relation names ending in the character are reserved for this reverse arc or converse relation labelling Therefore no relation name may end with a The term relation always refers to a normal forward arc label We will use the term unitpath to mean either a relation name or the name of its converse relation define relation Defines each relation to be an arc label The name of a relation must not end in the character Each relation is added to the SNePSUL variable relations An informative message is given if a relation has previously been defined Initially SNePS has a set of relations defined as if the following had been executed define forall exists pevb min max thresh threshmax emin emax etot ant sant cq dcq arg default if when vars suchthat do condition then else action act plan goal precondition effect objectl object2 For uses of the predefined relations see Sections 3 1 1
21. form is read References to Lisp variables can be made via a reader macro mechanism similar to the comma within backquote syntax Results of expansions will be automatically interned into the SNePSUL package i e any symbols that might be part of such a result unless explicitly specified otherwise All the special reader syntax available at the SNePS top level is available too 47 48 CHAPTER 5 PROGRAM INTERFACE The semantics of the syntax is s expression S expression will be read with ordinary reader syntax and at execution time it will be evaluated and its value inserted into the SNePSUL expression If the value is a symbol or a list containing symbols then these sym bols will be interned into the SNePSUL package first Ex describe m1 m2 will act like describe ml m2 s expression Just like but the value of the s expression has to be a list which will be spliced into the SNePSUL ex pression Any symbols occuring as leaves in the list will be interned into the SNePSUL package first Ex describe m1 m2 will act like describe m1 m2 s expression Just like but symbols in the value will not be interned into the SNePSUL package s expression Just like but symbols in the value will not be interned into the SNePSUL package CAUTION The syntax can only be used within SNePSUL forms but not to denote multiple forms e g While coml com2 com3 i
22. gt 01 Z Om E Pl1 d Pi a 01 a operate during both forward and backward chaining 6 4 2 Recursion Recursive rules such as all x y z ancestor x y ancestor y z amp gt ancestor x Z may be used without causing an infinite loop Infinite loops caused by backward chaining on rules such as all x duck motherOf x gt duck x or by forward chaining on rules such as all x number x gt number successorof x are terminated under the control of the global parameters depthCutoffBackx and depthCutoffForwardx respectively Ifa subgoal is generated during backward chaining whose depth in terms of parenthesis nesting exceeds depthCutoffBack it is not pursued Also if a result is gener ated during forward chaining whose depth in terms of parenthesis nesting exceeds depthCutoffForwardx itis not pursued xdepthCutoffBackx and depthCutoffForward are each set by default to 10 and can be changed independently via set f 6 5 SNERE IN SNEPSLOG 67 6 5 SNeRE in SNePSLOG 6 5 1 SNePSLOG Versions of SNeRE Constructs With the introduction of Mode 3 in SNePSLOG see p 60 almost every structure that can be built via SNePSUL can be build via SNePSLOG For some restrictions see 36 5 2 Therefore SNeRE agents can be defined and operated via SNePSLOG In fact the examples of Chapter 4 are available as a SNePSLOG demo in the distributed version of SNePS Each SNeRE caseframe docu
23. ACS ere BR AAA A ie RR Re Boies Be A POR 4 3 Associating Primitive Action Nodes with Their Functions 44 Defined Actina 43 i we Fa gentle a E a Rhee eke E oe we 111 vii AA ODO UuBbNnNnk 21 21 21 23 24 25 CONTENTS APS GOS 8 ack Ee ele oR BP vet BA Be AE Bnd Be trie ie es BO ge a 39 4 6 The Execution Cycle Preconditions and Effects o e 40 Program Interface 47 oid Transformers 3662 jase AD PARADES RA EA Ds 47 5 2 With SNePSUL Reader Macro oaoa ee 47 5 2 1 Controlling the Evaluation of SNePSUL Forms Generated by 48 9 2 2 Example Use of Hh voii bn ee bee e Se 49 5 3 Defining New Commands 2 000000 A E D eee ee 51 SNePSLOG 55 6 1 SNePSLOG Basics ur A A a e e ea 55 6 2 SNePSLOG Syntax 6 5 ts a a a a h 55 6 3 SNePSLOG Semantics n a e he ee ee A Oe Ne ale BG ls 58 6 3 1 Semantics of SNePSLOG Commands o 58 6 3 2 Semantics of wifNameCommands 0 000 eee eee 61 6 3 3 Semantics of wffCommands 0 2 00 eee ee ee 62 6 3 4 Semantics of SNePSLOG Wffs 0 2 00 eee eee eee 63 6 4 SNIP im SNePSELOG y gga ade fake Gee ae eA ee seks Re Ge a ee wd Bs 64 6 4 1 Rules of Inference 2 0 0 00 ee ee eee 64 6 4 2 sRECUTSION fo 35 SIG a eG So they Sth Soba Ge RLS Be ed he hy 66 6 5 SNeREin SNePSLOG A w a OG A we OS Ow ek Se 67 6 5 1 SNePSLOG Versions of SNeRE Constructs
24. For example the action function for say used in the examples above was defined by 4 2 PRIMITIVE ACTS 29 define primaction say objectl object2 Print the the argument nodes in order format t amp A A sneps choose ns objectl sneps choose ns object2 The predefined primitive action functions and what they do are Functions for Mental acts believe object1 where object1 must be a proposition node The following special cases of belief revision are first performed e If Mn MIN 0 MAX 0 ARG objecti is currently asserted it is disbelieved e If Mn MIN i MAX 1 ARG objecti otherprop forany i and otherprop are currently asserted otherpropis disbelieved Then object 1 is asserted and forward inference is done with it assert min 0 max 0 arg build agent Stu state is location here M2 CPU time 0 09 x describe deduce agent Swho state is location here M2 MIN 0 MAX 0 ARG M1 AGENT STU LOCATION HERE STAT M2 CPU time 0 18 E IS x perform build action believe object1 build agent Stu state is location here CPU time 0 06 x describe deduce agent who state is location here M1 AGENT STU LOCATION HERE STATE IS M1 CPU time 0 06 disbelieve object1 where object 1 must be a proposition node objectlis removed from context describe deduce agent who state is
25. If a lexeme is a root form and has only one feature list the lexical entry may be used for both analysis and synthesis If the principle inflected parts of the noun plural or verb third person singular past past participle present participle are irregular those parts must have their own lexical entries which will only be used for morphological analysis If a lexeme has multiple lexical entries because it has multiple lexical categories they can only be used for analysis You should give each feature list its own root form even if it is a made up word and then give each of these root forms a lexical entry with a single feature list to be used for morphological synthesis Those feature lists should have root forms that are correctly spelled forms The example lexicon in Section 9 5 has a regular and an irregular noun a regular and an irregular verb a word saw that is both a noun and a verb and a multi word lexeme Computer Science 9 4 2 Functions for Morphological Analysis englex lookup word Looks up the word which must be a string in the lexicon and returns its lexical entry possibly expanded with default values If word is not in the lexicon tries to remove prefixes and suffixes until it finds a root form that is in the lexicon whereupon it returns an appropriately modified lexical entry Uses the standard features and values listed above as used for morphological analysis englex Lookup has been imported into the parse
26. Lisp being used considers the symbols constructed by this algorithm to be the same When SNePSLOG prints a SNePSLOGsymbol it does not print string quotes nor escape characters 55 CHAPTER 6 SNEPSLOG Table 6 1 The Syntax of SNePSLOG Commands command wffNameCommand snepslogCommand wffCommand wffNameCommand wffName terminalPunctuation wffCommand wff terminalPunctuation wff must not be an atomic symbol snepslogCommand n SNePSULcommand LispForm activate wff activate wff terminalPunctuation add to context SNePSLOGsymbol termSet ask wff terminalPunctuation askifnot wff terminalPunctuation askwh wff terminalPunctuation askwhnot wff terminalPunctuation beliefs about pTermSet clear infer learkb copyright define frame SNePSLOGsymbol LispList LispString define path SNePSRelation SNePSPath demo filePath i t b bv a av on describe context SNePSLOGsymbol describe terms pTermSet expert lispl list asserted wffs SNePSLOGsymbol list contexts list terms pTermSet list wffs load filePath normal perform atomicTerm remove from context SNePSLOGsymbol pTermSet set context SNePSLOGsymbol pTermSet set default context SNePSLOGsymbol set mode 1 L set mode 2 set mode 3 t ni1 show pTermSet trace SNePSLOGfunction undefine path SNePSRelation unlabele
27. Mary studied Time sec 0 167 Who uses the computer Mary used a computer Time sec 0 267 Who likes a dog I don t know Time sec 0 167 Who sees a saw John saw a saw Time sec 0 217 99 The SNePS network built as a result of this interaction is shown in Figure 9 2 Note especially that when definite noun phrases occurred in statements they were represented by nodes that were already in the net because of previous indefinite noun phrases 100 CHAPTER 9 SNALPS THE SNEPS NATURAL LANGUAGE PROCESSING SYSTEM E CD lex lex x Om lex 16 propername class lex Ye et ua O class 131 E no nember agent 171 ES object act act member agent 51 ES act 54 object 121 652 object object propername ES SITI Figure 9 2 SNePS network after running the example Chapter 10 SNePS as a Database Management System SNePS can be used as a network version of a relational database system in which every element of the relational database is represented by a base node each row of each relation is represented by a molecular node and each column label attribute is represented by an arc label Whenever a row r of a relation R has an element e in column c the molecular node representing r has an arc labeled R to the special node relation and an arc labelled c pointing to the base node representing e Table 10 1 shows two relations from the Supplier Part Project database of Date p 114 Figure
28. NeW 4 visit a ee ob ates eee es ee ds 1 3 System Portability 0 2 2 02 0 eee eee 1 4 Commands and Environments 2000 LS Types OF Nodes iu sae o o BS BE ee See Bs 167 Contexts ari ito Ry 2 Ba A ee Be ls hei BAER 17 SNePSUL Variables 6 0 ic ewe RIA SRG BO A 2 SNePSUL Commands 2 1 Context Specifiets eo 4 24 bs PEP ESAs ee eG SSeS bes 2 2 Loading SNEPS i eos ghost BP od AAA EA eB 2 3 Entering and Leaving SNePS o o 2 4 Using Auxiliary Files o o 2 4 1 Reading Writing Files o 2 4 2 Writing Altering Source Files For Use With SNePS 2 6 and ACL 6 2 5 Relations 2 na be at ioe eo ds dea 25 1 Reduction Inference omar e ai ee a 2 5 2 Path Based Inference o o 2 6 Operating on Contexts ies A ee A 2 7 Building Networks aoaaa aaa a 2 8 Deleting Information e 2 9 Functions Returning Sets of Nodes or of Unitpaths 2 10 Displaying the Network 2 2 ee 2 11 Retrieving Information 0 2 0 000 ee eee eee SNIP The SNePS Inference Package 3 1 Representing and Using Rules o o ALT Conmectives i wa ir aa ee ae E eb a A Bob Quantifers a a a A HR Bs 3 13 Recursion sey a es eR A eS ee td 32 Tracing Inference A AAA A AR POR ies A SNeRE The SNePS Rational Engine AT A A ae PA ee Re a ee EP a Se AD PIVE
29. a result of any such terms You offer 3 7 Larger Works You may create a Larger Work by combining Covered Code with other code not governed by the terms of this License and distribute the Larger Work as a single product In such a case You must make sure the requirements of this License are fulfilled for the Covered Code 4 Inability to Comply Due to Statute or Regulation If it is impossible for You to comply with any of the terms of this License with respect to some or all of the Covered Code due to statute judicial order or regulation then You must a comply with the terms of this License to the maximum extent possible and b describe the limitations and the code they affect Such description must be included in the LEGAL file described in Section 3 4 and must be included with all distributions of the Source Code Except to the extent prohibited by statute or regulation such description must be sufficiently detailed for a recipient of ordinary skill to be able to understand it 5 Application of this License This License applies to code to which the Initial Developer has attached the notice in Exhibit A and to related Covered Code 6 Versions of the License 6 1 New Versions University at Buffalo UB may publish revised and or new versions of the License from time to time Each version will be given a distinguishing version number 6 2 Effect of New Versions Once Covered Code has been published under a particu
30. action to do four times define frame say action object say x1 will be represented by lt action say gt lt object x1 gt define primaction sayAction object print sneps choose ns object sayAction 6 6 THE TELL ASK INTERFACE 69 jj Attach it attach primaction say sayAction t pi Test it perform snsequence4 say one say two say three say four one two three four Zero Argument Predicates and Functions SNePSLOG syntax does not accept atomic propositions but it does accept zero argument predicates and zero argument functions as long as the parentheses are included A way to perform zero argument actions In SNePSLOG is shown here set mode 3 Net reset In SNePSLOG Mode 3 gt Define a zero argument action define primaction reportAction princ I m here reportAction gt zir Attach it attach primaction report reportAction t gt N Define the frame so that the action arc goes to the action define frame report action report x1 will be represented by lt action report gt lt nil x1 gt ets Test ME perform report I m here 6 6 The Tell Ask Interface The Tell Ask interface is an easy way to interface with SNePS from Common Lisp programs and should be used for this purpose whenever parts of the knowledge base are built via SNePSLOG snepslog tell string string must be a valid SNePSLOG input Tell gives st ring to the SNePSLOG inter
31. build action say object1l No one s object2 here Hello Stu CPU time 0 43 34 CHAPTER 4 SNERE THE SNEPS RATIONAL ENGINE perform build action disbelieve objectl build agent Stu state is location here CPU time 0 03 perform build action disbelieve objectl build agent Bill state is location here CPU time 0 03 perform build action withsome vars Sx suchthat build agent x state is location here do build action say objectl Hello object2 xx else build action say object1l No one s object2 here No one s here CPU time 0 42 Notice that withal11 and withsome only operate on entities already believed to satisfy the suchthat criterion If you want to operate on entities discovered in the future to satisfy the suchthat criterion use when do or whenever do and note that they only perform on new beliefs x describe assert member Stu Bil1 class person M1 CLASS PERSON MEMBER Bill Stu M1 CPU time 0 07 describe assert agent Stu state is location here M2 AGENT Stu LOCATION HERE STATE IS M2 CPU time 0 06 x describe assert forall p ant build member p class person cq build when build agent p state is location here do build action say objectl Hello new object2 p M3 FORALL V1 ANT P1 CLASS PERSON MEMBER V1 CQ P4 DO P3 ACTION SAY OBJECT1 Hell
32. c Representations Contributor represents that except as disclosed pursuant to Section 3 4 a above Contributor believes that Contributor s Modifications are Contributor s original creation s and or Contributor has sufficient rights to grant the rights conveyed by this License 3 5 Required Notices You must duplicate the notice in Exhibit A in each file of the Source Code If it is not possible to put such notice in a particular Source Code file due to its structure then You must include such notice in a location such as a relevant directory where a user would be likely to look for such a notice If You created one or more Modification s You may add your name as a Contributor to the notice described in Exhibit A You must also duplicate this License in any documentation for the Source Code where You describe recipients rights or ownership rights relating to Covered Code You may choose to offer and to charge a fee for warranty support indemnity or liability obligations to one or more recipients of Covered Code However You may do so only on Your own behalf and not on behalf of the Initial Developer or any Contributor You must make 1t absolutely clear than any such warranty support indemnity or liability obligation is offered by You alone and You hereby agree to indemnify the Initial Developer and every Contributor for any liability incurred by the Initial Developer or such Contributor as a result of warranty support indemnit
33. constant until SNePSLOG assigns it to a term After that 1t will always refer to its assigned term and no longer be recognized as the individual constant An assigned wffName in a wffNameCommand refers to its assigned term and the meaning of the wff NameCommand depends on the terminalPunctuation in exactly the same way as for wffCommands see 86 3 3 6 3 3 Semantics of wffCommands A wffCommand is a wff followed by one of the terminalPunctuation marks 1 99 or The effect of the wffCommand depends on the terminalPunctuation as follows The wff is asserted into the knowledge base as an hypothesis of the current context and it is printed However if in Mode 3 and the wff is a policy it is adopted instead of asserted The wff is asserted into the knowledge base as an hypothesis of the current context and forward inference is done on it It and all wffs newly asserted as a result of this wffCommand are printed 2 If the wff is asserted in the knowledge base it is printed otherwise nothing is printed If qvars see Table 6 2 occur in the wff they are taken as free variables and all ground instances that are asserted are printed 2 i Backward inference is done on the wff If qvars see Table 6 2 occur in the wff they are taken as free variables If either instances of the wff or of its negation are inferred or already asserted those wffs are printed If no instances of it or its negation ar
34. evaluator and the COMMON LISP eval uator Although every SNePSUL function is a COMMON LISP function the SNePSUL loop provides certain special facilities so it is best to be in the proper top level loop for extended work sneps Lisp function that brings the user into the SNePS read eval print loop lisp SNePSUL function that returns the user to the Lisp evaluator 9 10 CHAPTER 2 SNEPSUL COMMANDS SNePSUL command that causes the next form to be evaluated by Lisp SNePSUL command that puts the user into an embedded Lisp read eval print loop until the next occurrence of the form whereupon the user is returned to the SNePSUL loop 2 4 Using Auxiliary Files 2 4 1 Reading Writing Files The commands in this section provide for the use of auxiliary files for the storage of networks or of sequences of commands outnet file Stores the current network on the file in a special SNePS format The syntax for the file specification is machine dependent innet file If file was created by a call to outnet the current network will be initialized to the one stored on file Note innet rewrites the entire network and several SNePSUL variables so it cannot be used to combine several networks An error message is issued if file is not in the appropriate format intext file Reads a sequence of SNePSUL commands from the file and executes them without doing any printing All assertions specified by the file are done as one batc
35. flexible a choice of relations as may be done using SNePSUL syntax Section 6 3 1 SNePS 2 5 includes a change in how nodes are implemented and how sets of nodes are ordered that should improve the speed of the system activate Section 2 7 the SNePSLOG per form command Sec tion 6 3 1 a revised semantics for when do with the old when do now renamed whenever do Section 4 1 SNePS 2 6 code has been modified so that it can be loaded into ALLEGRO CL version 6 X and used with its default case mode of case sensitive lower all predefined COMMON LISP symbols have lower case names and the case of characters typed into the LIspP listener is left as originally typed or loaded into earlier versions of ALLEGRO CL or other versions of COMMON LISP that use case insensitive upper mode all predefined COMMON LISP symbols have upper case names and the case of characters typed into the LISP listener is changed to upper case Some SNePS symbols may look different in the two different modes such as M1 vs m1 The output for the examples in this manual was generated from earlier versions of SNePS so it is typically in uppercase except where special formatting was used to generate the output SNePS 2 6 1 Previously the functions and amp took only two arguments Now they can take zero or more The function show has been added The SNeRE mental action believe now checks for and disbelieves more contradictory beliefs than before Th
36. for some of its attributes and yields the rows of the relations in which those attributes take on those values A selection from relation Ry in which attribute ay takes on value v1 is expressed in SNePSUL as find R relation 411 U1 in Vin For example to select rows of the SUPPLIER relation where the CITY is Paris or Athens and the STATUS is 30 we could do describe find supplier relation city paris athens status 30 M3 CITY PARIS S S3 SNAME BLAKE STATUS 30 SUPPLIER RELATION M5 CITY ATHENS S S5 SNAME ADAMS STATUS 30 SUPPLIER RELATION M3 M5 CPU time 0 08 If we want a new permanent relation say supplier2 to be this selection from the SUPPLIER relation we could do x define supplier2 SUPPLIER2 CPU time 0 03 x describe dbAssertVirtual dbproject find supplier relation city paris athens status 30 s sname status city supplier2 relation M17 CITY PARIS S S3 SNAME BLAKE STATUS 30 SUPPLIER2 RELATION M18 CITY ATHENS S S5 SNAME ADAMS STATUS 30 SUPPLIER2 RELATION M17 M18 CPU time 0 23 T 10 1 3 Join Join is a database operation that given two relations R and Ra with attributes a11 Qin and a21 02m respectively and an atttibute a a1 a2 produces a relation with attributes a11 1n 21 2j 1 09
37. frame Sum attachedfunction addl add2 sum and the SNePSUL user would do define addl add2 sum The relation attachedfunction is defined by the SNePS system 2 Attached functions must be defined by define attachedfunction fun lambda variables amp body where a fun will be the name of the attached function 73 74 CHAPTER 7 PROCEDURAL ATTACHMENT b each lambda variable must either be a relation used for the arguments of the predicate node or such a relation enclosed in a pair of parentheses If the lambda variable is an atomic relation name it will be bound to the modified set of nodes that that relation points to If it is a relation enclosed in parentheses the relation symbol will be bound to one element of the modified set of nodes that that relation points to presumably there will only be one The way that the nodes will be modified is i if the node is a variable it will be left alone ii if the node s name looks like a Lisp number the number will be provided iii otherwise a Lisp symbol whose name is the same string as the node s name will be provided In the body of the attached function the three Lisp predicates numberp symbolp and sneps isvar n may be used to distinguish the three types of argument c the attached function must return a list each of whose members is a list of two members i Either snip pos to indicate that the instance of the proposition is to be asserted or snip neg to indicat
38. in a predicate logic notation to almost all the facilities provided by SNePSUL It is now the recommended way to interact with SNePS SNeBR Chapter 8 the SNePS Belief Revision system recognizes when a contradiction exists in the network identifies possible culprits and performs disbelief propagation It eliminates contradictions auto matically in some cases and helps the user to do so in the general case SNaLPS Chapter 9 the SNePS Natural Language Processing System consists of a morphological an alyzer a morphological synthesizer and a Generalized Augmented Transition Network GATN Grammar 1 2 CHAPTER 1 INTRODUCTION interpreter compiler Using these facilities one can write natural language and other interfaces for SNePS 1 2 What s New SNePS 2 differs in several respects from its predecessor now called SNePS 79 mostly because of theoretical decisions that were made since SNePS 79 was implemented SNePS 2 1 differs from SNePS 2 0 by including belief revision as a standard feature SNePS 2 3 includes some techniques for making node based inference faster and includes SNeRE Chap ter 4 SNePS 2 4 includes a change in how contexts and sets of contexts are implemented that should improve the speed of the system deducetrue deducefalse deducewh and deducewhnot Sec tion 2 11 the Tell Ask interface Section 6 6 and SNePSLOG Mode 3 which allows SNePSLOG syntax to be used to build SNePS networks using as
39. location here M1 AGENT STU LOCATION HERE STATE IS M1 CPU time 0 07 perform build action disbelieve objectl build agent Stu state is location here CPU time 0 03 describe deduce agent Swho state is location here CPU time 0 07 adopt object1 where object 1 must be a policy node when do whenever do or if do The policy is adopted and an attempt is made to use it 30 CHAPTER 4 SNERE THE SNEPS RATIONAL ENGINE unadopt objecti1 where object 1 must be a policy node when do whenever do or if do The policy is no longer adopted Functions for Control acts do all object1 where object 1 is a set of one or more act nodes causes all of the act nodes to be performed in some arbitrary order perform build action do all objectl build action say objectl Hello object2 Bill build action say object1l Hello object2 Stu Hello Stu Hello Bill CPU time 0 32 do one object1 where object 1 is a set of one or more act nodes causes an arbitrary one of the act nodes to be performed If the variable snip choose randomly is T default do one will choose its act randomly if it is NIL it will choose deterministicly which might be desirable during debugging See page 45 for clarification perform build action do one objectl build action say objectl Hello object2 Bill build action say obje
40. of environments The body of the command has a documentation string just as a normal defun and it uses the with snepsul reader macro see Section 5 2 to easily call the SNePSUL command assert in the body of the command definition Example 3 gt defsnepscom lex build word top bns t Builds a node with a lex arc to a WORD node 2 build lex word gt CPU time 0 25 5 3 DEFINING NEW COMMANDS 53 lex build progn format t Word read Word Lucy M1 CPU time 0 03 This last example command uses all the features It has an argument list it explicitly specifies two environ ments in which the command will be legal and it evaluates its arguments which is the reason why we could call it with the little interactive input specification Note that this command builds not asserts a node and that it will be available as a top level command because we specified top as one of the environments For good reasons the standard build command is not a top level command hence in this example we forced SNePS to do something which is normally not allowed By convention every command that returns a node set should return a context as a second value which will be used to display the node set Commands which use an application of the reader macro as their last body form will achieve this automatically Otherwise a form such as values nodes crntct has to be used as the last body form
41. or trademark Licensable by Initial Developer to use reproduce modify display perform sublicense and distribute the Original Code or portions thereof with or without Modifications and or as part of a Larger Work and b under Patents Claims infringed by the making using or selling of Original Code to make have made use practice sell and offer for sale and or otherwise dispose of the Original Code or portions thereof c the licenses granted in this Section 2 1 a and b are effective on the date Initial Developer first distributes Original Code under the terms of this License d Notwithstanding Section 2 1 b above no patent license is granted 1 for code that You delete from the Original Code 2 separate from the Original Code or 3 for infringements caused by 1 the modification of the Original Code or ii the combination of the Original Code with other software or devices 3 DISTRIBUTION OBLIGATIONS 109 2 2 Contributor Grant Subject to third party intellectual property claims each Contributor hereby grants You a world wide royalty free non exclusive license a under intellectual property rights other than patent or trademark Licensable by Contributor to use reproduce modify display perform sublicense and distribute the Modifications created by such Con tributor or portions thereof either on an unmodified basis with other Modifications as Covered Code and or as part of a Larger Work and b
42. than such Participant s Contributor Version directly or indirectly infringes any patent then any rights granted to You by such Participant under Sections 2 1 b and 2 2 b are revoked effective as of the date You first made used sold distributed or had made Modifications made by that Participant 9 LIMITATION OF LIABILITY 113 8 3 If You assert a patent infringement claim against Participant alleging that such Participant s Contributor Version directly or indirectly infringes any patent where such claim is resolved such as by license or settlement prior to the initiation of patent infringement litigation then the reasonable value of the licenses granted by such Participant under Sections 2 1 or 2 2 shall be taken into account in determining the amount or value of any payment or license 8 4 In the event of termination under Sections 8 1 or 8 2 above all end user license agreements excluding distributors and resellers which have been validly granted by You or any distributor hereunder prior to termination shall survive termination 9 LIMITATION OF LIABILITY UNDER NO CIRCUMSTANCES AND UNDER NO LEGAL THEORY WHETHER TORT INCLUD ING NEGLIGENCE CONTRACT OR OTHERWISE SHALL YOU THE INITIAL DEVELOPER ANY OTHER CONTRIBUTOR OR ANY DISTRIBUTOR OF COVERED CODE OR ANY SUPPLIER OF ANY OF SUCH PARTIES BE LIABLE TO ANY PERSON FOR ANY INDIRECT SPECIAL INCIDEN TAL OR CONSEQUENTIAL DAMAGES OF ANY CHARACTER INCLUDING WITHOUT LIMIT
43. that were eliminated The SNePSUL dbproject function has been designed for this purpose For example to show the STATUS and CITY of all suppliers one can do STATUS STATUS CPU time 2 3 dbproject 0 0 0 find supplier rel CITY LONDON S CITY PARIS ST 07 ation TATUS ATUS 30 status city 10 CITY CIETY PA ATHI RIS ENS The dbpro ject function forms and returns a virtual relation which is represented as a SNePS data type called a set of flat cable sets Compare the following two ways of getting complete details of the SUPPLIER relation The first uses the SNePSUL describe function to print the details of the nodes that make up the relation describe find supplier relation M1 CITY LONDON S S1 SNAME SMITH STATUS 2 M2 CITY PARIS S S2 SNAME JONES STATUS 10 M3 CITY PARIS S S3 SNAME BLAKE STATUS 30 M4 CITY LONDON S S4 SNAME CLARK STATUS 2 M5 CITY ATHENS S S5 SNAME ADAMS STATUS 3 M1 M2 M3 M4 M5 CPU time 0 15 0 0 0 SUPPL SUPPL SUPPL SUPPL SUPPL The second uses dbproject to display a virtual relation with the same information x dbproject find supplier relati
44. the form of a frame a node with arcs slots emanating from it to other nodes the slot fillers Each entity represented in the network is represented by a unique node term This is enforced by SNePS 2 in that whenever the user specifies a node to be added to the network that would look exactly like one already there in the sense of having the same set of arcs slots going from it to the same set of other nodes fillers SNePS 2 retrieves the old one instead of building the new one SNePSUL the SNePS User Language is the lowest level command language for using SNePS It is a Lispish language usually entered by the user at the top level SNePSUL read eval print loop but it can also be called from Lisp code or from GATN arcs The SNePSUL chapters of this manual follow the style of Guy Steele s COMMON LISP book and assume that the reader is familiar with that book and with COMMON LISP The organization of this manual has been retained from the time when SNePSUL was the standard way to interact with SNePS However the use of SNePSLOG is now recommended SNIP Chapter 3 the SNePS Inference Package interprets certain nodes as representing reasoning rules called deduction rules SNIP supports a variety of specially designed propositional connectives and quanti fiers and performs a kind of combined forward backward inference called bi directional inference SNePSLOG Chapter 6 is a logic programming interface to SNePS and provides direct access
45. under Patent Claims infringed by the making using or selling of Modifications made by that Contrib utor either alone and or in combination with its Contributor Version or portions of such combination to make use sell offer for sale have made and or otherwise dispose of 1 Modifications made by that Contributor or portions thereof and 2 the combination of Modifications made by that Contributor with its Contributor Version or portions of such combination c the licenses granted in Sections 2 2 a and 2 2 b are effective on the date Contributor first makes Commercial Use of the Covered Code d Notwithstanding Section 2 2 b above no patent license is granted 1 for any code that Contributor has deleted from the Contributor Version 2 separate from the Contributor Version 3 for infringements caused by 1 third party modifications of Contributor Version or ii the combination of Modifications made by that Contributor with other software except as part of the Contributor Version or other devices or 4 under Patent Claims infringed by Covered Code in the absence of Modifications made by that Contributor 3 Distribution Obligations 3 1 Application of License The Modifications which You create or to which You contribute are governed by the terms of this License including without limitation Section 2 2 The Source Code version of Covered Code may be distributed only under the terms of this License or a future version of
46. variables appear in the specification The numb ar gument is optional If numb is omitted then deduce continues until no more answers can be derived If numb is a single integer it specifies the total number of answers requested If numb is zero no inference is done only answers already in the network are returned Otherwise numb must be a list of two numbers npos nneg and deduction terminates after at least npos positive and nneg negative instances are derived 20 CHAPTER 2 SNEPSUL COMMANDS Chapter 3 SNIP The SNePS Inference Package Automatic inference may be triggered using the function deduce see Section 2 11 a generalization of find or the function add see Section 2 7 a generalization of assert In order for these to accomplish anything deduction rules must exist in the network A deduction rule is a network structure dominated by a tule node A rule node represents a logical formula of molecular nodes using connectives and quantifiers 3 1 Representing and Using Rules Rules are placed in the network with the assert and add commands see Section 2 7 The arcs needed to build rules are predefined by SNePS 3 1 1 Connectives Connectives are the means by which simple propositions are compounded to make more complicated ones In classical logic this compounding is accomplished by use of standard connectives such as amp AND and V OR A number of disadvantages exist in using standard connectives in SNePS prim
47. 1 Hi M8 ACT SAYHI PLAN M7 ACTION SAY OBJECT1 Hiya Intending to do M15 ACTION DO ONE OBJECT1 M3 ACTION SAY OBJECT1 Hello M5 ACTION SAY OBJECT1 Hi M7 ACTION SAY OBJECT1 Hiya Now doing DO ONE M3 ACTION SAY OBJECT1 Hello M5 ACTION SAY OBJECT1 Hi M7 ACTION SAY OBJECT1 Hiya Chose to do the act M3 ACTION SAY OBJECT1 Hello About to do M3 ACTION SAY OBJECT1 Hello I wonder if the act M3 ACTION SAY OBJECT1 Hello has any preconditions The act M3 ACTION SAY OBJECT1 Hello has no preconditions Hello About to do M10 ACTION SAY OBJECT1 STU I wonder if the act M10 ACTION SAY OBJECT1 STU has any preconditions 4 6 THE EXECUTION CYCLE PRECONDITIONS AND EFFECTS 43 The act M10 ACTION SAY OBJECT1 STU has no preconditions STU CPU time 3 10 The current version of SNeRE will never give up trying to achieve the preconditions of an act it is trying to perform even if some precondition is impossible to achieve The effects of an act a are all e for which propositions of the form M act a effect e are deduceable x describe assert forall person ant build member person class person ca build act build action cal
48. 10 1 shows a fragment of the SNePS network Table 10 1 From Date s Supplier Part Project Database PROJECT SUPPLIER J JNAME CITY S SNAME STATUS CITY jl sorter Paris sl Smith 20 London j2 punch Rome s2 Jones 10 Paris j3 reader Athens s3 Blake 30 Paris j4 console Athens s4 Clark 20 London j5 collator London s5 Adams 30 Athens j6 terminal Oslo j7 tape London version of this database 10 1 SNePS as a Relational Database The three basic operations on relational databases are select project and join The next three subsections show how these operations may be expressed in SNePSUL 10 1 1 Project Project is a database operation that given one relation produces another that has all the rows of the first but only specific columns Actually some of the rows might collapse if the only distinguishing elements were 1C J Date An Introduction to Database Systems 3rd Edition Reading MA Addison Wesley 1981 101 102 CHAPTER 10 SNEPS AS A DATABASE MANAGEMENT SYSTEM ON eo RELATION f A 5 Mc f NG project 4 A Ssi t proj met 15 4 no A MO supplier supplier r TAPE mt om Fd d ON pa gf T T status mM e Pa SK snane a ty oity F p Ay pe NX j 1 Y CER LONDON A Figure 10 1 Fragment of SNePS network for the Supplier Part Project Database 10 1 SNEPS AS A RELATIONAL DATABASE 103 in columns
49. 17 58 10 58 achieve 39 67 act effect 43 act plan 36 act precondition 40 activate 2 16 58 activate 2 58 active connection graph 58 62 ActPlan 68 add 16 add to context 15 58 adopt 16 29 67 all hyps 9 and 14 apply function to ns 47 arc labels 12 ask 2 58 70 askifnot 2 58 70 askwh 2 58 70 askwhnot 2 58 70 71 assert 6 7 16 assertions SNEPSUL variable 7 parser atn read sentence 11 atnin 79 attach function 74 attach primaction 35 attachedfunction 73 belief revision 75 belief space 75 beliefs about 19 58 believe 2 29 67 75 blieve 75 bnscommands SNEPSUL variable 7 break arc 79 build 5 6 13 16 snip choose randomlyx 30 clear infer 17 58 clear infer all 17 clearkb 58 commands infix 5 macro 6 postfix 6 prefix 5 SNEPSUL 5 commands SNEPSUL variable 7 compose 14 context 75 current 7 default 7 context 9 context specifier 9 contexts 7 extensionally defined 7 intensionally defined 7 names 7 nodes in 7 operating on 15 converse 14 copyright 58 current context 75 current configuration 80 dbAssertVirtual 106 dbcount 106 116 INDEX db join 106 dbmax 106 dbmin 106 dbproject 106 dbtot 106 dc lisp 11 dc no pause 11 dc pause help 11 dc quit 11 dc quit all 11 dc read pause 11 dc set pause 11 dc sneps 11 dc snepslog 11 deduce 12 19 deducefalse 2 19 dedu
50. 18 CHAPTER 2 SNEPSUL COMMANDS Infix function that assigns the nodeset to be the value of the SNePSUL variable symbol _ nodeset unitpathset Infix function that returns the set of those nodes in the nodeset which do not have any of the unitpaths in the unitpathset emanating from them gt unitpathset symbol Infix function that assigns the unitpathset to be the value of the SNePSUL variable symbol unitpath A list of unitpaths in a context where a unitpathset is required is treated as an expression whose value is a set of the unitpaths in the list 2 10 Displaying the Network The commands in this section are various ways of printing or otherwise displaying the information in the network dump nodeset context specifier Prints the name of each node in the nodeset that is in the context specified by context specifier along with all arcs going from it or into it and the nodes that each arc points to or from For a complete dump of the network execute dump nodes context all hyps describe nodeset context specifier Similar to dump but describes only the molecular and pattern nodes in the nodesets describes all molecular and pattern nodes dominated by nodes it describes describes any node at most once the second and later times only the node s name is printed full describe nodeset context specifier Similar to describe but also shows the context s each node is asserted in Unlike dump and describ
51. 2 thresh i j Pi Pn Pi Pizto Pi Pitn j 1 Pn operates during both forward and backward chaining i gt Elimination P1 Pn i gt Q1 Om Pl Pit Q1 operates during both forward and backward chaining v gt Introduction IfP1 Q1 Qm then P1 v gt Q1 Om operates during back ward chaining amp gt Introduction If P1 Pn Q1 and and P1 Pn Om such that Q1 and and Qm have an origin set in common then F P1 Pn amp gt Q1 Qm operates during backward chaining 66 CHAPTER 6 SNEPSLOG nexists Elimination f nexists i j k Z P1 8 Pn 7 Q 2 P a Nees P a O a P G se Pr Gj O 4 Py Gj41 gt Pnldj 1 F O adj41 operates during both forward and backward chaining De nexists i As RD AOS P a1 a Pn 1 Q P k i hee Pa Gri O G _1 Py Gx i41 gt Prn G e i41 Fo Q dr i 1 operates during both forward and backward chaining al1 Elimination 1 all 2 andor i J P1 E Pn E p P1 0 Pa 7 Pn d for0 lt i lt j lt n 0 lt j lt n 2 all 2 andor i 7 Pi Z Pn 7 Pi Pn Pn a for0 lt i lt n ISJEN 3 all 7 thresh i j Pi Z 7 Pn Z Pi P Pi41 Pit n j 1 EP a 4 all 7 thresh teehee ABD PO ls Pila z Pp a 5 all E P1 8 Pn i
52. 341 gt 02m and every row 11 gt 1n 21 25 1 234 1 C2m where 11 1n Was a row of Ri and 21 2 1 15 C23 1 C2m was a row of Rp For example Table 10 2 shows the join of the relations in Table 10 1 on the attribute CITY This join may be created and displayed by the SNePSUL db join command which like dbpro ject creates a virtual relation dbjoin city find supplier relation s sname status city find project relation j jname S S1 SNAME SMITH STATUS 20 CITY LONDON J J7 JNAME TAPE S S1 SNAME SMITH STATUS 20 CITY LONDON J J5 JNAME COLLATOR S S2 SNAME JONES STATUS 10 CITY PARIS J J1 JNAME SORTER S S3 SNAME BLAKE STATUS 30 CITY PARIS J J1 JNAME SORTER S S4 SNAME CLARK STATUS 20 CITY LONDON J J7 JNAME TAPE S S4 SNAME CLARK STATUS 20 CITY LONDON J J5 JNAME COLLATOR S S5 SNAME ADAMS STATUS 30 CITY ATHENS J J4 JNAME CONSOLE S S5 SNAME ADAMS STATUS 30 CITY ATHENS J J3 JNAME READER CPU time 0 3 10 2 SNEPS AS A NETWORK DATABASE SUPPLI 105 Table 10 2 The join of SUPPLIER and PROJECT on CITY S SNAME STATUS CITY J JN
53. A TION DAMAGES FOR LOSS OF GOODWILL WORK STOPPAGE COMPUTER FAILURE OR MAL FUNCTION OR ANY AND ALL OTHER COMMERCIAL DAMAGES OR LOSSES EVEN IF SUCH PARTY SHALL HAVE BEEN INFORMED OF THE POSSIBILITY OF SUCH DAMAGES THIS LIMITA TION OF LIABILITY SHALL NOT APPLY TO LIABILITY FOR DEATH OR PERSONAL INJURY RE SULTING FROM SUCH PARTY S NEGLIGENCE TO THE EXTENT APPLICABLE LAW PROHIBITS SUCH LIMITATION SOME JURISDICTIONS DO NOT ALLOW THE EXCLUSION OR LIMITATION OF INCIDENTAL OR CONSEQUENTIAL DAMAGES SO THIS EXCLUSION AND LIMITATION MAY NOT APPLY TO YOU 10 U S government end users The Covered Code is a commercial item as that term is defined in 48 C F R 2 101 Oct 1995 consisting of commercial computer software and commercial computer software documentation as such terms are used in 48 C F R 12 212 Sept 1995 Consistent with 48 C F R 12 212 and 48 C F R 227 7202 1 through 227 7202 4 June 1995 all U S Government End Users acquire Covered Code with only those rights set forth herein 11 Miscellaneous This License represents the complete agreement concerning subject matter hereof If any provision of this License is held to be unenforceable such provision shall be reformed only to the extent necessary to make it enforceable This License shall be governed by law provisions of the state of New York except to the extent applicable law if any provides otherwise excluding its conflict of law provisions With re
54. ACT P39 ACTION GREET OBJECT1 V6 PRECONDITION P38 AGENT V6 LOCATION HERE STATE IS M34 CPU time 0 13 x perform build action greet objectl Stu About to do M9 ACTION GREET OBJECT1 STU I wonder if the act M9 ACTION GREET OBJECT1 STU has any preconditions The act M9 ACTION GREET OBJECT1 STU has a precondition M35 ACT M9 ACTION GREET OBJECT1 STU PRECONDITION M33 AGENT STU LOCATION HERE STATE IS It is satisfied The act M9 ACTION GREET OBJECT1 STU has a plan M12 ACT M9 ACTION GREET OBJECT1 STU PLAN M11 ACTION SNSEQUENCE OBJECT1 SAYHI OBJECT2 M10 ACTION SAY OBJECT1 STU Intending to do M14 ACTION DO ONE OBJECT1 M11 ACTION SNSEQUENCE OBJECT1 SAYHI OBJECT2 M10 ACTION SAY OBJECT1 STU Now doing DO ONE M11 ACTION SNSEQUENCE OBJECT1 SAYHT OBJECT2 M10 ACTION SAY OBJECT1 STU Chose to do the act M11 ACTION SNSEQUENCE OBJECT1 SAYHT OBJECT2 M10 ACTION SAY OBJECT1 STU 42 CHAPTER 4 SNERE THE SNEPS RATIONAL ENGINE About to do SAYHI I wonder if the act SAYHI has any preconditions The act SAYHI has no preconditions The act SAYHI has the following plans M4 ACT SAYHI PLAN M3 ACTION SAY OBJECT1 Hello M6 ACT SAYHI PLAN M5 ACTION SAY OBJECT
55. AME sl Smith 20 London j5 collator sl Smith 20 London j7 tape s2 Jones 10 Paris jl sorter s3 Blake 30 Paris jl sorter s4 Clark 20 London j5 collator s4 Clark 20 London j7 tape s5 Adams 30 Athens 33 reader s5 Adams 30 Athens j4 console Again to make the virtual relation permanent dbAssert Virtual is used define supplierproject ERPROJ CPU time describe dbAssertVirtual dbjoin city ECT 0 03 s sname status city E TAPE S S1 SNAME SMITH STATUS 20 ATOR S S1 SNAME SMITH STATUS 20 E SORTER S S2 SNAME JONES STATUS 10 E SORTER S S3 SNAME BLAKE STATUS 30 E TAPE S S4 SNAME CLARK STATUS 20 ATOR S S4 SNAME CLARK STATUS 20 S S5 SNAME ADAMS STATUS 30 S S5 SNAME ADAMS STATUS 30 find supplier relation find project relation j jname supplierproject relation 19 CITY LONDON J J7 JNA SUPPLIERPROJECT RELATION 20 CITY LONDON J J5 JNAME COL SUPPLIERPROJECT RELATION 21 CITY PARIS J J1 JNAM SUPPLIERPROJECT RELATION 22 CITY PARIS J J1 JNAM SUPPLIERPROJEC
56. ANTICS OF GATN GRAMMARS 81 trace levelx An integer indicating what printing is to be done by SNaLPS The possibilities are listed below Each trace level also does what every lower level does 2 SNaLPS does no printing 1 Prints the time taken by each parse 0 Default Prints the trace level the starting state and the results of each parse If the result is a SNePS node it is printed using SNEPS DESCRIBE Various error messages may also be printed at this trace level 1 A warning is printed if a word is looked up in the lexicon but not found there 2 Reserved for future trace information 3 Reserved for future trace information 4 Prints the string representation of the original sentence and prints a trace of the execution of each arc traversed and its associated configuration 5 Prints the current configuration of the GATN parser prior to taking any transitions Any HOLDs SENDRs or LIFTRs associated with a configuration being output are printed 6 Prints the arc currently being attempted regardless of success Also prints all blocked arcs 7 Reserved for future trace information 8 Parent configurations of a configuration being output are also printed This is voluminous but useful for debugging deeply embedded via PUSH CALL or RCALL configurations 9 All the acts associated with a configuration that was PUSHed CALLed or RCALLed to are printed This is not normally useful 10 Reserved for future trace in
57. ENVIRONMENTS 5 e Lucid or Sun Common Lisp e GNU CLISP e CMU Common Lisp e Macintosh Common Lisp e Harlequin LispWorks The JUNG JIMI version of show and the Java SNePS API require some facilities peculiar to Franz s ACL For installations that do not have that compiler an executable file is available for downloading from http www cse buffalo edu sneps Downloads That executable provides all the features of SNePS 2 7 Every other feature of SNePS 2 7 is written in ANSI Common Lisp 1 4 Commands and Environments A SNePSUL command is classified according to its role either as a procedure or as a function A procedure is a command that performs some action but returns nothing using the COMMON LISP values function A function is a command that always returns some value possibly after having performed some action as a side effect A function is implemented directly as a Lisp function A command is also classified according to the environment s in which it may legally appear A proce dure can be entered only at the top level of SNePSUL A function however may appear in many different environments The five environments are 1 The top level of SNePS 2 2 A relation set position embedded in a command 3 A node set position in build 4 A node set position in findor findassert 5 A node set position in any of the other commands Finally a command can be classified according to the relation between its position and the position o
58. Entailment Aj An i C1 Cm means that the conjunction of any i of the antecedents implies the conjunc tion of the consequents In other words if or more of the antecedents are true then all of the consequents are true A numerical entailment rule is built with the SNePSUL command assert thresh 7 amp ant A An cq Cros Cm Use An asserted numerical entailment may be used in forward or backward inference to conclude that one or more of its consequents is to be asserted AndOr y Pi Pa means that at least and at most j of the n propositions are true An andor rule is built with the SNePSUL command assert min 7 max j arg PL Pa The following special cases of andor are representations of standard connectives i j n is AND i 7 0isa generalization of NOR andi 7 1 is a generalization of EXCLUSIVE OR Use An asserted and or may be used in forward or backward inference to conclude that one or more of its arguments is to be asserted or that the negation of one or more of its arguments is to be asserted An unasserted and or for which 2 j number of arguments will be asserted during backward inference if all its arguments are asserted Thresh e P P means that either fewer than or more than j of the n propositions are true j may be omitted in which case it defaults to n 1 A thresh rule is built with the SNePSUL command assert thresh threshmax 7 arg Pr
59. F seep SEN PEL senep Ep tetany CET Path Based Inference If l define frame P r rl ri rn has been done 2 define path ri phas been done 3 P al al an is asserted 4 and the path p exists in the network from ai to b then P al ai b an can be derived andor n n Elimination andor n n P1 Pn P1 operates during both forward and back ward chaining andor n n Introduction P1 Pnt andor n n P1 Pn operates during back ward chaining and forward in backward chaining andor 0 0 Elimination andor 0 0 P1 Pn Pl operates during both forward and backward chaining andor 0 0 Introduction 1 P1 Pnrandor 0 0 P1 Pn operates during backward chaining and forward in backward chaining 2 If Pis asserted with the origin set a and P is asserted with the origin set 6 and H is in a U 8 and is chosen as the culprit of the contradiction then His derived in the origin set a U 6 4 andor i j i lt n j gt 0 Elimination l andor i j P1 Pn Pl Pj Pn for 0 lt i lt j lt n 0 lt j lt n operates during both forward and backward chaining 2 andor i j Pi Pn Pi Pn iF Pn for0 lt i lt n i lt j lt n operates during both forward and backward chaining thresh Elimination l thresh i j Pi Pn Pi Pi Pipl gt Pi n j 1 Pn operates during both forward and backward chaining
60. G JIMI version both or neither If both are installed the user can dynamically pick the ver sion to be used by setting the global variable cl user xuse gui showx to t for the JUNG JIMI version or to nil for the dot version trace SNePSLOGfunction If SNePSLOGfunction is inference Inference tracing is turned on acting Tracing of acting is turned on translation The translation of each SNePSLOG command into SNePSUL is shown parsing The parsing of each SNePSLOG command is traced The name of any Lisp function That function is traced e undefine path SNePSRelation Deletes the path based inference rule from the SNePSRelation e unlabeled Turns on unlabeled mode in which when a term is printed neither its wffName see normal mode nor its support set see expert mode is printed untrace SNePSLOGfunction If SNePSLOGfunction is inference Inference tracing is turned off acting Tracing of acting is turned off translation The translation of each SNePSLOG command into SNePSUL is not shown parsing The parsing of each SNePSLOG command is not traced The name of any Lisp function That function is not traced 6 3 2 Semantics of wffNameCommands A wffNameCommand is a wffName followed by one of the terminalPunctuation marks 1 99 or 9 A wffName is a symbol made up of wf f followed by an integer WffNames are assigned to terms by SNePSLOG If the wffName wffi for some 1 has already
61. G Technical Note 30 Notes on Converting to ACL 6 by Stuart C Shapiro which can be found as Reference Number 2001 5 at http www cse buffalo edu sneps Bibliography This will be referred to from now on as SNeRG TN 30 If a programmer wishes to load a pre existing SNePS input file using SNePS 2 6 running in ACL 6 they have two options 1 Make sure the file and any other files involved are ACL 6 compatible refer to the SNeRG TN 30 described above 2 Wrap the main file i e the single file which contains SNePS input and or loads any other input files in the code shown below Code Wrap For ACL 6 and SNePS 2 6 Compatibility If a pre existing source file does not run successfully using ACL 6 and SNePS 2 6 and altering all the files involved is not desirable the following code can be wrapped around the main input file this should result in a successful run Some minor code changes might be necessary per SNeRG TN 30 but any inconsistency of case e g NIL vs nil in SNePS code or in the input lines will be adjusted by the wrap The code wrap is intended to be read at the top LISP level Insert the following code at the beginning of the main source file NOTE These suggestions work if the pre existing file runs successfully using SNePS 2 5 They are especially important if the run includes loading altered SNePS code Older files might need further adjusting 12 CHAPTER 2 SNEPSUL COMMANDS 77 adjustment for ACL6
62. I PLAN M3 ACTION SAY OBJECT1 Hello M4 CPU time 0 09 x describe assert act sayHi plan build action say objectl Hi M6 ACT SAYHI PLAN M5 ACTION SAY OBJECT1 Hi M6 CPU time 0 06 4 5 GOALS 39 x describe assert act sayHi plan build action say objectl Hiya M8 ACT SAYHI PLAN M7 ACTION SAY OBJECT1 Hiya M8 CPU time 0 09 and finally greet Stu and Bill x perform build action greet objectl Stu Hiya STU CPU time 1 34 x perform build action greet objectl Bill Hello BILL CPU time 1 37 A defined act node may be represented by a node with no action arc emanating from it as long as a plan can be derived for it x describe assert act ask plan build action say objectl Who s there M7 ACT ASK PLAN M6 ACTION SAY OBJECT1 Who s there M7 CPU time 0 05 x perform ask Who s there CPU time 0 33 4 5 Goals In the SNeRE formalism a goal is a proposition that the SNeRE agent is trying to bring about The action of trying to bring about a goal is called achieve achieve object1 where object1 must be a proposition node is performed by finding plans for achieving object1 and performing a do one on them The plans for achieving goals are given by assertions of the form M goa1 g plan p which says that p is a plan for achieving the goal g x describe assert forall person ant
63. Mary studies Computer Science Resulting parse S MOOD DECL NP VP Time DEFINITE T NPR Mary V study NP DEFINITE T NPR Computer Science sec 0 05 Mary used a computer Resulting parse S MOOD DECL NP VP Time DEFINITE T NPR Mary V use NP DEFINITE NIL N computer sec 0 05 94 CHAPTER 9 SNALPS THE SNEPS NATURAL LANGUAGE PROCESSING SYSTEM John saw a saw Resulting parse S MOOD DECL NP DEFINITE T NPR John VP V see NP DEFINITE NIL N saw Time sec 0 067 What bit John Resulting parse S MOOD QUESTION NP VP V bite NP DEFINITE T NPR John Time sec 0 05 Who sleeps Resulting parse S MOOD QUESTION NP VP V sleep Time sec 0 034 Who studied Resulting parse S MOOD QUESTION NP VP V study Time sec 0 05 Who uses the computer Resulting parse S MOOD QUESTION NP VP V use NP DEFINITE T N computer Time sec 0 067 Who likes a dog Resulting parse S MOOD QUESTION NP VP V like NP DEFINITE NIL N dog Time sec 0 067 9 5 EXAMPLES 95 Who sees a saw Resulting parse S MOOD QUESTION NP VP V see NP DEFINITE NIL N saw Time sec 0 067
64. NEPS ry UL SN EPSUL MYBASE EPS NOD Hp a S E VAR FRANZ 5 3 DEFINING NEW COMMANDS 51 5 3 Defining New Commands defsnepscom command arg environments eval args body form defsnepscon is a Lisp macro that defines SNePSUL commands All standard SNePSUL commands such as find assert deduce etc are defined via defsnepscom More importantly defsnepscom is the only way to define commands which will be recognized as legal SNePSUL commands at the SNePS top level The syntax of def snepsconm is very similar to that of a standard defun or defmacro command is a Lisp symbol which serves as the command name e g find deduce my find isa etc command will get exported automatically from its home package and imported into the SNePSUL package hence even if the command was defined in a different package it can be used at the SNePS top level without package qualifiers The only catch is that if command is the name of a standard COMMON LISP function as in the case of find or assert then that symbol has to be shadowed in its home package with the COMMON LISP function shadow before the command gets defined arg is an optional argument list in the standard COMMON LISP syntax An actual call to the command has to be legal according to that argument list otherwise an error will occur The only difference to the standard de fun style of specifying argument lists is an
65. NePS 2 2 5 2 Path Based Inference Path based inference allows an arc between two nodes to be inferred from the presence of a path of arcs between them The various versions of find as well as deduce will use any path based inference rules that have been declared define path relation path Declares the path based inference rule V n1 n2 path n1 na gt relation n n2 Le if a path of arcs specified by path is in the network going from node n to node no then the single arc labelled by relation is inferred as going from node n to node ng See the following subsection for the syntax of path No relation may have more than one path based inference rule for it at any time This is not a restriction since a disjunction of paths is also a path Warning A path based inference rule will not be expanded recursively I e no relation or converse relation in the path will be expanded even if a path based inference rule has been declared for it A subtle implication of this is that it is almost always proper todo define path relation or relation new path so that explicit occurrences of relation will be recognized undefine path relation path Deletes the given path based inference rules Syntax and Semantics of Paths A unitpath is simply a single arc followed in the forward or the reverse direction A path can be a sequence of unitpaths or a more complicated way of getting from one node to another Keep in mind the dist
66. Pi ai 2n Pm a1 2n The first occurrence of a variable must be preceded by the macro and subsequent occurrences must be preceded by the macro If there is only one restriction ant should be used instead of ant Use Universal instantiation has been implemented but not universal generalization The Existential Quantifier The existential quantifier has not yet been implemented in SNePS 2 However it is not needed because Skolem functions can be used instead Whenever an existentially quantified variable y is bound within the scope of universally quantified vari ables x1 n y can be replaced by the Skolem function f x1 n as long as f is used nowhere else The existential quantifier that binds y can then be eliminated So to represent an existentially quantified variable in SNePS 2 define a set of arcs say Skf al a2 and replace the variable node by a molecular node with the ai arcs going to the universally quantified variables whose scopes contain the existentially quantified variable and with the Sk f arc going to a new base node that serves as the Skolem function The Skolem function node may be named mnemonically For example to represent the formula Va Man a2 gt Jy Woman y A Loves x y you might do assert forall man ant build member xman class man cq build member build Skf loved by al man thiswoman class woman build agent man act loves object thiswoman
67. R First SNeBR informs the user that a contradiction was detected and specifies the contradictory context and the two contradictory propositions showing the support set of each Then SNeBR gives the user a choice not necessarily in the order shown 1 The user may choose to not do the input that led to the contradiction The context and BS are left as they were 2 The user may choose to continue in an inconsistent context That situation is not disastrous since SNePS uses a paraconsistent logic a contradiction does not imply irrelevant other propositions 3 The user may choose to repair the context now known to be inconsistent Tn the propositional case when all proposition valued functions have arity zero the contradictory BS is is left as is 8 6 DISBELIEF PROPAGATION 77 In case the user chooses to repair the context SNeBR then presents the user with each set of possible culprits as follows 1 If any of the possible culprits is in the origin set of both contradictory propositions the user is warned that choosing that as the culprit might be too extreme a revision 2 Then SNeBR lists each of the possible culprits listing all the propositions it supports and showing how many propositions are in the list Choosing this as the culprit and disbelieving it will result in all these supported propositions also being removed from the BS unless they have alternate derivations 3 The user may then e di
68. S networks may now be dis played by show either via dot or JUNG and JIMI depending on the installers choice both require separate downloading If both versions are installed the user may choose which one to use by setting the value of the global variable cl user xuse gui showx There have been other bug fixes improvements to speed and improvements to user messages A com plete description of what s new in SNePS 2 7 is at http www cse buffalo edu sneps Downloads releaseNotes262 html SNePS 2 7 1 SNePS The command beliefs about nodeset context specifier has been added It returns a set of all the nodes asserted in the given context that dominate the nodes described by the nodesets SNePSLOG 1 In SNePS 2 7 0 an attempt to use the amp gt Introduction rule caused a Lisp error That has been fixed and amp gt Introduction works in SNePS 2 7 1 2 In SNePS 2 7 0 the v gt Introduction rule did not work Moreover the v gt Introduction rule as documented in the SNePS 2 7 User s Manual is incorrect In SnePS 2 7 1 the v gt Introduction rule works as long as there is only a single antecedent The v gt Introduction rule for multiple antecedents is not yet implemented 3 In certain circumstances activate and activate caused an error That has been fixed 4 The snepslogCommand beliefs about pTermSet has been added It returns a set of all the asserted wffs that dominate the terms de
69. SNePS 2 7 1 USER S MANUAL Stuart C Shapiro and The SNePS Implementation Group Department of Computer Science and Engineering University at Buffalo The State University of New York 201 Bell Hall Buffalo NY 14260 2000 December 8 2010 The development of SNePS was supported in part by the National Science Foundation under Grants IRI 8610517 and REC 0106338 the Defense Advanced Research Projects Agency under Contract F30602 87 C 0136 monitored by the Rome Air Development Center to the Calspan UB Research Center the Air Force Systems Command Rome Air Development Center Griffiss Air Force Base New York 13441 5700 and the Air Force Office of Scientific Research Bolling AFB DC 20332 under Contract No F30602 85 C 0008 which supported the Northeast Artificial Intelligence Consortium NAIC NASA under contract NAS 9 19335 to Amherst Systems Inc ONR under contract NO0014 98 C 0062 to Apple Aid Inc the U S Army CECOM Intelligence and Information Warfare Directorate I2WD through a contract with CACI Technologies and through Contract DAAB 07 01 D G001 with Booze Allen amp Hamilton and CUBRC under prime contract FA8750 06 C 0184 between CUBRC and U S Air Force Research Laboratory Rome NY Over the years many people have contributed to the design and implementation of SNePS and to the writing of successive versions of the SNePS User s Manual They constitute The SNePS Implementation Group cited on the title page an
70. T RELATION 23 CITY LONDON J J7 JNA SUPPLIERPROJECT RELATION 24 CITY LONDON J J5 JNAME COL SUPPLIERPROJECT RELATION 25 CITY ATHENS J J4 JNAME CONSOLE SUPPLIERPROJECT RELATION 26 CITY ATHENS J J3 JNAME READER SUPPLIERPROJECT RELATION 19 M20 M21 M22 M23 M24 M25 M26 CPU time 0 92 10 2 SNePS as a Network Database Although SNePS can be treated as a relational database as shown in the previous section it is more naturally a network database For example to find the names of suppliers with the same status as suppliers in the same city as the sorter project using relational database techniques one would join the SUPPLIER and PROJECT 106 CHAPTER 10 SNEPS AS A DATABASE MANAGEMENT SYSTEM relations on CITY join the result with SUPPLIER again on STATUS select rows where PROJECT is sorter and project the result on the SNAME attribute However in SNePSUL one could just do x find sname status status city city jname sorter ADAMS BLAKE JONES CPU time 0 02 Additional examples of these techniques may be found in the SNePS DBMS demonstration 10 3 Database Functions Functions specifically supplied for treating SNePS as a Database Management System are documented in this section Additional ones may be created using the functions documented in Chapter 5 Note also innet and outnet documented in Section 2 4 for saving the database across runs dbAssertVirtual virtuale
71. amically pick the version to be used by setting the global variable cl user xuse gui showx indexuse gui showcl1 user use gui showx to t for the 2 11 RETRIEVING INFORMATION 19 JUNG JIMI version or to nil for the dot version surface nodeset Generates a description of each node in each nodeset using the currently loaded GATN grammar starting in state g 2 11 Retrieving Information The functions in this section find nodes in the network and return them find path nodeset context specifier findassert path nodeset context specifier findconstant path nodeset context specifier findbase path nodeset context specifier findvariable path nodeset context specifier findpattern path nodeset context specifier Returns the set of nodes in the specified context such that each node in the set has every specified path going from it to at least one node in the accompanying nodeset find class man greek will find nodes with a class arc to either man or greek whereas find class man class greek will find nodes with class arcs to both man and greek find returns all appropriate nodes in the specified context findassert returns only asserted nodes findconstant returns only base or molecular nodes findbase returns only base nodes findvariable returns only variable nodes findpattern returns only pattern nodes beliefs about nodeset context specifier Returns a set of all the nodes asserted in
72. andor 0 n 1 P1 Pn nor P1 Pn isan abbreviation of andor 0 0 P1 Pn xor P1 Pn isan abbreviation of andor 1 1 P1 Pn thresh i j P1 Pn denotes the proposition that either fewer than i or more than j of Pl Pn are true thresh i P1 Pn isanabbreviationof thresh i n 1 P1 Pn P1 lt gt lt gt Pn is an abbreviation of thresh 1 n 1 P1 Pn iff P1 Pn isanabbreviationof thresh 1 n 1 Pl Pn 64 CHAPTER 6 SNEPSLOG P1 Pn i gt Q1 Om denotes the proposition that if any i of P1 Pn are true than so are Q1 and Qm P1 Pn gt Q1 Qmb isanabbreviationof P1 Pn 1 gt Q1 Qm P1 Pn v gt Q1 Omjisanabbreviationof P1 Pn 1 gt 01 Qm P1 Pn amp gt Q1 Qmb isanabbreviationof P1 Pn n gt Q1 Qm all x1 xn P x1 xn denotes the proposition that every ground instance of P x1 xn that obeys the Unique Variable Binding Rule UVBR is true A ground instance of P x1 xn obeys UVBR if no term already in P x1 xn substitutes for any of the variables x1 xn and if no one term substitutes for more than one variable nexists i j k x1 xn P1 Pnn 01 Qmm denotes the propo sition that there are k substitution instances obeying UVBR of x1 xn that satisfy P1 Pnn and of them at
73. ar word 0 vowelsx test char format t n setf SaynBeforeVowelsx nil when string word a setf SaynBeforeVowels t format t 7A word defun say word s Prints the single word or the list of words If the word is a sets SaynBeforeVowels If SaynBeforeVowels is set then prints n before word s if the first letter of word s is a vowel if listp word s mapc SayOneWord word s SayOneWord word s 96 s1 ps vp CHAPTER 9 SNALPS THE SNEPS NATURAL LANGUAGE PROCESSING SYSTEM The initial arc is used to make two SNePSUL variables each of which holds a SNePS variable node This results in a major efficiency gain over creating new SNePS variable nodes each time a question or an indefinite NP is parsed jump s1 t or wh wh a SNePS variable to use for Wh questions or x x x a variable for indef NP s in questions push ps t Parse a sentence and send results to RESPOND jump respond cat wh t A Wh question starts with who or what setr agent x wh set AGENT to a variable node setr mood question liftr mood to vp push np t sendr mood decl The only acceptable statements are NP V NP MOOD must be sent down because an indefinite NP introduces a new individual in a statement but must be treated as a variable to be found in a question setr agent set AGENT to parse of subject
74. arguments and some processes are being traced it returns a list of processes being traced If called with no arguments and no processes are being traced it turns on event tracing of all processes Following these traces requires a knowledge of how SNIP is implemented They are intended for implementing and debugging new features of SNIP unev trace process name A SNePSUL top level command for turning off event tracing of MULTI processes If called with one or more arguments unquoted it turns off event tracing of those named processes If called with no arguments it turns off all event tracing in trace process name A SNePSUL top level command for tracing MULTI processes If called with one or more arguments un quoted it turns on initiation tracing of those named processes If called with no arguments and some 26 CHAPTER 3 SNIP THE SNEPS INFERENCE PACKAGE processes are being traced it returns a list of processes being traced If called with no arguments and no processes are being traced it turns on initiation tracing of all processes Following these traces requires a knowledge of how SNIP is implemented They are intended for implementing and debugging new features of SNIP unin trace process name A SNePSUL top level command for turning off initiation tracing of MULTI processes If called with one or more arguments unquoted it turns off initiation tracing of those named processes If called with no arguments it
75. arily because of their binary nature and the size of the network needed to store representations with standard connectives To avoid these problems SNePS uses non standard connectives These non standard connectives are as adequate as standard connectives but they take arbitrarily large sets of arguments and express common modes of hu man reason simply The non standard connectives are and entailment or entailment numerical entailment andor and thresh An explanation of each connective follows And Entailment Aj An amp gt C1 Cm means that the conjunction of the antecedents implies the conjunction of the consequents An and entailment rule is built with the SNePSUL command assert amp ant A An cq Ci ey Cm Use An asserted and entailment may be used in forward or backward inference to conclude that one or more of its consequents is to be asserted Or Entailment Aj An V C1 Cm means that the disjunction of the antecedents implies the conjunction of the consequents An or entailment rule is built with the SNePSUL command 21 22 CHAPTER 3 SNIP THE SNEPS INFERENCE PACKAGE assert ant A An cq C1 no Cm Note or entailment is more efficient than and entailment so if there is only one antecedent use ant rather than sant Use An asserted or entailment may be used in forward or backward inference to conclude that one or more of its consequents is to be asserted Numerical
76. at the proposition p will hold The SNeRE executive described in 84 6 will automatically cause p to be asserted after a is performed GoalPlan p a The act a is a plan for bringing about the proposition p GoalP lan assertions are inferred by achieve p to find plans for achieving p Precondition a p In order to be able to perform the act a the proposition p must hold It is assumed that the SNeRE agent is able to achieve p Before the SNeRE executive described in 4 6 performs any act which has inferrable preconditions it will attempt to achieve all the preconditions 6 5 2 Restrictions Snsequence The primitive control action snsequence p 30 can take an arbitrary number of acts but SNePSLOG cannot accept a function of an arbitrary number of arguments so in SNePSLOG Mode 3 snsequence is limited to two argument acts The way around this is to define versions of snsequence for more than two arguments This is illustrated for a sequence of four acts below set mode 3 Net reset In SNePSLOG Mode 3 77 Define the case frame for a 4 act sequenc define frame snsequence4 action objectl object2 object3 object4 snsequence4 xl x2 x3 x4 will be represented by lt action snsequence4 gt lt objectl x1 gt lt object2 x2 gt lt object3 x3 gt lt object4 x4 gt i77 but attach it to the original snsequence primitive action attach primaction snsequence4 snsequence t 77 Define a specific primitive
77. ating action nodes with action functions M3 M4 and M5 are act nodes with action nodes SAY M1 and B1 respectively 38 CHAPTER 4 SNERE THE SNEPS RATIONAL ENGINE To illustrate the use of defined acts we will first define say as a one object action function and associate action nodes with the functions say and snsequence gt define primaction say objectl Print the object format t e A sneps choose ns object1 SAY gt attach primaction say say snsequence snsequence T gt SA CPU time 0 05 Then we will give a rule that says the way to greet a person is to sayHi then say the person s name and assert that Stu and Bill are people x describe assert forall S person ant build member person class person cq build act build action greet objectl person plan build action snsequence objectl sayHi object2 build action say objectl x person M1 FORALL V1 ANT P1 CLASS PERSON MEMBER V1 CQ P5 ACT P2 ACTION GREET OBJECT1 V1 PLAN P4 ACTION SNSEQUENCE OBJECT1 SAYHI OBJECT2 P3 ACTION SAY OBJECT1 V1 M1 CPU time 0 18 x describe assert member Stu Bill class person M2 CLASS PERSON MEMBER BILL STU M2 CPU time 0 06 We will give three plans for sayHi x describe assert act sayHi plan build action say objectl Hello M4 ACT SAYH
78. been assigned to a term then using wf f i ina wffNameCommand or in any SNePSLOG expression is equivalent to using the term that it names Actually it is even better Whenever SNePSLOG parses a new variable not just a new occurrence of a variable it creates a new variable node see S1 5 even if the variable has the same name as a previous one So a variable containing term in one wffCommand will never be the same identical term as one included in a previous wffCommand However the wffName assigned to a term will always refer to that term Note the difference between the two techniques here all x Robin x gt Bird x wffl all x Robin x gt Bird x Source all x Robin x gt Bird x World Book wff3 Source all x Robin x gt Bird x World Book 62 CHAPTER 6 SNEPSLOG list terms wff1 all x Robin x gt Bird x wff2 all x Robin x gt Bird x wff3 Source all x Robin x gt Bird x World Book clearkb Knowledge Base Cleared all x Robin x gt Bird x wffl all x Robin x gt Bird x Source wff1 World Book wff2 Source all x Robin x gt Bird x World Book list terms wff1 all x Robin x gt Bird x wff2 Source all x Robin x gt Bird x World Book If a wffName wffi is used before having been assigned to a term it will be interpreted as being an individual constant It will continue to be that individual
79. build member person class person cq build goal build agent person state is location here plan build action call objectl person M22 FORALL V5 ANT P23 CLASS PERSON MEMBER V5 CQ P26 GOAL P24 AGENT V5 LOCATION HERE STATE IS PLAN P25 ACTION CALL OBJECT1 V5 M22 CPU time 0 18 40 CHAPTER 4 SNERE THE SNEPS RATIONAL ENGINE x describe assert forall Sperson ant build member person class person cq build act build action call objectl x person plan build action snsequence objectl build action say objectl Come here object2 build action say objectl x person M24 FORALL V6 ANT P27 CLASS PERSON MEMBER V6 CQ P31 ACT P28 ACTION CALL OBJECT1 V6 PLAN P30 ACTION SNSEQUENCE OBJECT1 M23 ACTION SAY OBJECT1 Come here OBJECT2 P29 ACTION SAY OBJECT1 V6 M24 CPU time 0 19 x perform build action achieve objectl build agent Bill state is location here Come here BILL CPU time 1 76 4 6 The Execution Cycle Preconditions and Effects SNeRE acting may be understood by the following pseudo definition of perform although the actual im plemention is different perform act preconds set of preconditions of act unachieved preconditions preconds p p precond amp p is deduceable if unachieved precon
80. cet rue 2 19 deducewh 2 19 deducewhnot 2 19 default defaultct 7 defaultct SNEPSUL variable 7 9 15 define 12 define attachedfunction 73 define frame 58 define path 13 59 define primaction 28 defsnepscom 51 demo 10 59 depthCutoffBackx 25 66 depthCutoffForwardx 25 66 derived belief 75 describe 18 describe context 15 59 describe terms 59 disbelieve 2 29 67 do al1 30 45 67 do one 30 45 67 domain restrict 15 dot 3 18 dump 18 Effect 68 endLispConnection 71 erase 16 ev trace 25 exception 14 expert 59 files auxiliary 10 117 compatibility 11 find 5 19 findassert 5 19 findbase 19 findconstant 19 findpattern 19 findvariable 19 fnscommands SNEPSUL variable 7 Franz ACL 5 18 70 full describe 18 function SNEPSUL 5 getValFromVar 71 goal plan 39 GoalPlan 68 hypotheses 7 hypothesis 75 if do 28 ifdo 67 in trace 25 inference path based 13 reduction 12 13 xinfertracex 25 innet 10 intext 10 irreflexive restrict 14 isConnected 71 Java SNePS API 5 70 JavaSnepsAPI 70 JIMI 3 5 18 JUNG 3 5 18 kinconsistent 7 kplus 14 kstar 14 lexin 79 lisp 9 59 lisp list to ns 47 lisp object to node 47 list asserted wffs 59 list context names 15 list contexts 59 list hypotheses 15 list nodes 17 list terms 59 list wffs 2 59 118 load 60 multi print regs 26
81. ct1l Hello object2 Stu Hello Bill CPU time 0 15 snsequence objectl objectn where object1 objectnare act nodes causes objectl1 objectn to be performed in that order perform build action snsequence objectl build action say objectl Hello object2 Bill object2 build action say objectl Hello object2 Stu object3 build action say objectl Hello object2 Oscar Hello Bill Hello Stu Hello Oscar CPU time 0 31 Warning In the current version of SNePS if two objects of snsequence are the same act it will only be done once so instead of build action snsequence objectl al objecti ai objectit l aitl object ai objectn an one should use build action snsequence objectl al objecti ai objecti 1 build action snsequence objectl ai l objectj i ai objectn i an 4 2 PRIMITIVE ACTS 31 snif object1 where object1 is a set of guarded acts and a guarded act is either of the form condition p then a or of the form else elseact where p is a proposition node and a and elseaact are act nodes snif chooses at random one of the guarded acts whose condition is asserted and performs its act If none of the conditions is asserted and the else clause is present the el seact is performed x describe assert agent Stu state is location here M6 AGENT Stu LOCATION HERE STATE IS M6 CPU time 0 02
82. ction form Evaluates the SNePSUL form using function and returns the result Additionally prints the prompt form result and timing information just like the top level SNePS loop does good for monitoring the execution of the actual SNePSUL commands 5 2 WITH SNEPSUL READER MACRO 49 5 2 2 Example Use of gt in package user lt Package USER 79D151 E v gt defun myassert relation nodes let base node var mybase define relation relation myrel snip test assert relation f nodes snip test base node var describe setq nodes cdr nodes assert relation nodes myrel base node var assert arg build myrel hans relation franz myrel x mybase describe nodes MYASSERT 7 If the variable with snepsul eval function is bound to the 7 function with snepsul trac val then the generated SNePSUL 77 expression will only be printed but not actually executed gt let sneps with snepsul eval functionx sneps with snepsul trac val myassert relrel hans franz otto SNEPS DEFINE SNEPSUL RELREL Note snepsulization with single USER RELREL Package preservation with double SNEPSUL MYREL Unqualified symbols go into SNePSUL SNIP TEST 7 Qualified symbols keep their package SNEPS ASSERT SNEPSUL RELREL
83. d unt race SNePSLOGfunction SNePSLOGfunction inference acting translation parsing LispSymbol Q 6 2 SNEPSLOG SYNTAX 57 Table 6 2 The Syntax of SNePSLOG Wffs wif infixedTerm entailment pTermSet termSet termSequence prefixedTerm negatedTerm andorTerm setTerm threshTerm allTerm nexistsTerm nexistsParameters atomicTerm withsome allTerm termSetSequence symbolSequence wffName qvar SNePSLOGsymbol terminalPunctuation infixedTerm entailment prefixedTerm prefixedTerm and or lt gt prefixedTerm termSet gt v gt amp gt i gt termSet termSet but taken to denote all terms that match prefixedTerm termSequence prefixedTerm prefixedTerm negatedTerm andorTerm setTerm threshTerm allTerm nexistsTerm atomicTerm atomicTerm andor i j termSet 0 lt i lt j and or nand nor xor iff termSet thresh i j termSet 0 lt i lt j all symbolSequence wff wff must not be an atomic symbol nexists nexistsParameters symbolSequence termSet termSet ng RYN A Fy l i _ k wifName qvar SNePSLOGsymbol withsome allTerm in Mode 3 only qvar SNePSLOGsymbol termSetSequence wif withsome withall symbolSequence termSet termSet termSet termSet termSet SNePSLOGsymbol SNePSLOGsymbol w i w 3 must already name a wff SNePSLOGsymbol w
84. d Belief Spaces A proposition may be asserted as an hypothesis or as a derived belief An hypothesis is a proposition asserted into the SNePS knowledge base KB by the user or by the believe mental act A derived belief is one whose assertion status depended on derivation via the implemented rules of inference from other beliefs A context see also 81 6 is a named structure that contains a set of hypotheses There is always a current context within which all reasoning is performed In SNePSUL many commands take an optional context argument to change this default behavior A belief space BS defined by a context is the union of the set of hypotheses of the context and the set of derived beliefs that were derived from the hypotheses of the context The current BS is the BS defined by the current context Whenever it is said that a proposition is asserted it should be understood as being asserted in the current BS 8 2 Responsibilities of SNeBR SNeBR the SNePS Belief Revision System has five responsibilities 1 Recognize when the current BS is contradictory 2 Identify the possible culprits of a contradiction 3 Help the user choose the culprit to be blamed for a contradiction 4 Disbelieve any proposition derived from an hypothesis that is disbelieved disbelief propagation 5 Warn the user that is about to make the current BS be one that is already known to be contradictory These responsibilities are discussed in the following sect
85. d I am grateful to them They are listed here If I have inadvertently omitted anyone s name or have mispelled anyone s name please let me know and I will correct it for the next printing of this Manual Syed S Ali Michael J Almeida Charles W Arnold Robert J Bechtel Sudhaka Bharadwaj Jong S Byoun Alistair E Campbell Scott S Campbell Hans Chalupsky Chung M Chan Joongmin Choi Chi C Choy Soon Ae Chun Maria R Cravo Dmitriy Dligach Zuzana Dobes Gerard F Donlon Nicholas E Eastridge Elissa Feit David Forster Richard B Fritzson James Geller Susan M Haller Richard G Hull Haythem Ismail Frances L Johnson Steven D Johnson Darrel L Joy Sudha Kailar Michael W Kandefer Deepak Kumar Stanley C Kwasny John S Lewocz Naicong Li John D Lowrance Christopher Lusardi Anthony S Maida Mark D Malamut Nuno Mamede Joao P Martins Pedro A Matos Donald P McKay James P McKew Ernesto J Morgado William A Neagle Jeannette G Neal Jane Terry Nutter Rafail Ostrovsky Sandra L Peters Carlos Pinto Ferreira William J Rapaport Victor H Saks Harold L Shubin Reid G Simmons Benjamin R Spigle Jr Rohini K Srihari William M Stanton Jennifer M Suchin Lynn M Tranchell Jason C Van Blargan Nicholas F Vitulli Diana K Webster Janyce M Wiebe Albert Hanyong Yuhan Martin J Zaidel Stuart C Shapiro Contents List of Figures 1 Introduction LE General ices AS ee Veeck a ead oe ees fy 12 Whats
86. d SNePSLOGsymbol If no argument is given the default context is used e list contexts Lists the names of all the contexts that have been defined since the last time the knowledge base was cleared e list terms pTermSet If pTermSet is omitted all the closed functional terms in the knowledge base are printed If pTermSet is included all but only those closed functional terms that match the term patterns in pTermSet are printed e list wffs Lists all propositions that are asserted in any context That is all propositions that have been asserted as hypotheses or have been derived regardless of which context they are in 60 CHAPTER 6 SNEPSLOG load filePath Executes the contents of the specified file as a series of SNePSLOG commands without doing any printing All assertions specified by the file are done as one batch at the end of the loading process and they are all asserted into the current context normal Returns to the default normal mode In the normal mode each term is printed using its SNePSLOG representation and preceded by its wff name which is wf fn for some integer n Note that wf fn is the same node referred to in SNePSUL as mn The wff name is followed by an exclamation mark if and only if the term is a proposition asserted in the current context perform atomicTerm If SNePSLOG is in Mode 3 the act denoted by atomicTerm either an individual constant or ground functional term is performed See 86 5
87. default commands defined with defsnepscom do not evaluate their arguments If one wants com mand arguments to be evaluated before they get passed similar to the behavior of standard functions defined with defun one has to specify the optional third argument eval args as t body forms are a sequence of body forms possibly including a documentation string and declarations just as in a normal defun The value s of the last form will be returned Here are some examples Example 1 7 escape to the Lisp level since defnepscom is not a SNePSUL command gt defsnepscom mylist first amp o0ptional second amp rest others list first second others 52 CHAPTER 5 PROGRAM INTERFACE T SSS NA 7 back to the SNePS top level CPU time 0 03 x mylist apples oranges hans franz let s try it out APPLES ORANGES HANS FRANZ CPU time 0 01 Note that we did not have to quote apples and oranges in the example above because eval args was not specified as t Example 2 gt defsnepscom isa who what assert Asserts that WHO is a WHAT assert member who class what Ss NA CPU time 0 08 x describe isa hans student M1 CLASS STUDENT MEMBER HANS M1 CPU time 0 02 The isa command defined above takes two arguments who and what and it is legal in all places where the assert command is legal because we specified assert as the value
88. details on using the API and its classes and methods The JavaSnepsAPI subdirectory of the SNePS home directory contains the documentation and a file that provides an exam ple of using the Java SNePS API named TestAPI java The documentation is also located at http www cse buffalo edu sneps Docs javadocs 72 CHAPTER 6 SNEPSLOG Chapter 7 Procedural Attachment Normally if the system backchains into a proposition valued function node it will use inference to deter mine what instances of the negation of the node may be asserted If however the node has an arc labelled attachedfunction to a node that has been attached to a function the system will determine what in stances of the negation of the node may be asserted by evaluating the attached function For instance backchaining into the node pl attachedfunction Sum addl 3 add2 4 sum v1 could result in a computation to determine that the node ml attachedfunction Sum addl 3 add2 4 sum 7 should be asserted To use the procedural attachment facility the user must do three things 1 if using SNePSLOG define the frames for the predicates to which functions will be attached if using SNePSUL define the relations to be used as function arguments 2 define the attached function 3 attach the function to a SNePS predicate node These steps are more fully explained below 1 For an example of 1 using the above example the SNePSLOG user would do define
89. directly from Original Code provided by the Initial Developer and 110 UNIVERSITY AT BUFFALO PUBLIC LICENSE UBPL VERSION 1 0 including the name of the Initial Developer in a the Source Code and b in any notice in an Executable version or related documentation in which You describe the origin or ownership of the Covered Code 3 4 Intellectual Property Matters a Third Party Claims If Contributor has knowledge that a license under a third party s intellectual property rights is required to exercise the rights granted by such Contributor under Sections 2 1 or 2 2 Contributor must include a text file with the Source Code distribution titled LEGAL which describes the claim and the party making the claim in sufficient detail that a recipient will know whom to contact If Contributor obtains such knowledge after the Modification is made available as described in Section 3 2 Contributor shall promptly modify the LEGAL file in all copies Contributor makes available thereafter and shall take other steps such as notifying appropriate mailing lists or newsgroups reasonably calculated to inform those who received the Covered Code that new knowledge has been obtained b Contributor APIs If Contributor s Modifications include an application programming interface and Contributor has knowledge of patent licenses which are reasonably necessary to implement that API Contributor must also include this information in the LEGAL file
90. ditions nil then perform snsequence doall a p unachieved preconditions amp a achieve p act lse effects effects of act if act is primitive then apply primitive function act objects act doall a p effects amp a believe p else plans plans for carrying out act perform snsequence do one plans doall a p effects a believe p Notes and comments e A trace of the acting system is printed when the global variable plantracex is setto T If plantracex is set to surface then nodes are sent to the GATN generator starting at state G for printing If plantracex is NIL no trace is printed This was the setting for the previous examples in this chapter but the default is T e The preconditions of an act a are all p for which propositions of the formM act a precondition p are deduceable 4 6 THE EXECUTION CYCLE PRECONDITIONS AND EFFECTS describe assert forall person 41 ant build member person class person cq build act build action greet objectl x person precondition build agent person state is location here M34 FORALL V6 ANT P27 CLASS PERSON MEMBER V6 CQ P40
91. e full describe can describe nodes that are not in any context show nodeset show nodeset amp key file format Displays the network connected to the nodes in the nodesets in a graphical form The first version uses JUNG and JIMI and produces a graph that can be manipulated by hand The second version saves a spec ification of the network in the DOT language to a file from which an output file is produced via the dot compiler The file keyword argument specifies the base name of the dot and output files by default a temporary file The format keyword specifies the format of the output file which must be either gif default or ps The output file is displayed via either xv or gv dot produces a static figure Only one of these versions of show is available which one depends on the SNePS installer Neither dot JUNG nor JIMI are part of the SNePS distribution dot is part of the Graphviz package which can be downloaded from http graphviz org JUNG and it s associated packages Xerxes Colt and Jakarta Common Collections can be downloaded from http jung sourceforge net JIMI can be downloaded from http java sun com products 3imi The JUNG JIMI version of show requires either that SNePS is being run under Franz s ACL or that one of the executable packages of SNePS is being used The SNePS installer may install either the dot version or the JUNG JIMI version both or neither If both are installed the user can dyn
92. e SNePSLOG commands activate activate ask askifnot askwh and askwhnot have been added There have been sev eral other bug fixes Documentation of the SNePSLOG command list wffs has been added to the manual it was previously available Section 6 5 SNeRE in SNePSLOG has been added to this manual some material describing features that were never implemented in SNePS 2 has been deleted from the manual and there have been other editorial changes A complete description of what s new in SNePS 2 6 1 isathttp www cse buffalo edu sneps Downloads releaseNotes261 html SNePS 2 7 The SNePSLOG parser has been completely rewritten improved and made more robust The chapter on SNePSLOG has been completely rewritten SNePSLOG is now the preferred interface to SNePS Normal output from SNePSLOG now shows the wffName The SNePSLOG command define terms has been added and define frame given an optional string argument which when used together provide a facility for giving a natural language gloss of SNePS terms The tell ask interface has been improved and its documentation has been moved to 6 6 Previously askwh and askwhnot would return a simple list of terms even if the query had more than one free vari able Now they return a list of substitutions even if the query has only one free variable Similarly for the SNePSUL commands deducewh and deducewhnot A Java SNePS API has been added 1 2 WHAT S NEW 3 to give Java programs acces
93. e asserted or inferred nothing is printed If i is included after the question mark backward inference stops as soon as at least i instances of the wff or its negation are inferred If i 7 is included after the question mark backward inference stops as soon as at least i positive instances and j negative instances of the wff are inferred Forward and backward inference create and use an active connection graph acg The acg prevents infinite recursion when recursive rules are used and answers some queries without further inference It also focusses the interaction on the current reasoning problem However occasionally it causes inferences not to be made even though the knowledge base contains enough information to make them In that latter case it may help to perform the clear infer SNePSLOG Command and then try again 6 3 SNEPSLOG SEMANTICS 63 6 3 4 Semantics of SNePSLOG Wffs In this section we will mainly present the intended semantics of SNePSLOG wffs We realize however that any particular user might have different semantics in mind If SNePS is useful for that user under those semantics that s fine with us We intend a SNePS knowledge base to represent the mental entities conceived of by some individual agent Some of those entities will be propositions and some propositions will be asserted that is believed by the agent A SNePS knowledge base has one or more contexts A context has a set of hypotheses which are propo
94. e commands rscommands The set of commands valid at relation set positions in commands nscommands The set of commands valid at at node set positions in other commands CHAPTER 1 INTRODUCTION Chapter 2 SNePSUL Commands 2 1 Context Specifiers In a number of commands described in this chapter part of the syntax is context specifier and the seman tics mentions the context specified by context specifier In every such case the possible syntax of context specifier and what context is specified by each possibility is omit If the context specifier is omitted the specified context is the default context the value of defaultct context The context specified is the default context the value of defaultct context context name The context specified is that named context name which must be a symbol context nodeset context name The context specified is that named context name which is initialized to be the context whose set of hypotheses is the value of nodeset which must be a SNePSUL expression that evaluates to a set of proposition nodes context all hyps The context specified is the one whose set of hypotheses is the set of all hypotheses all assertions entered by the user 2 2 Loading SNePS Ask whoever maintains SNePS at your site how to load SNePS Typically this involves running COMMON LIspP and then loading SNePS 2 3 Entering and Leaving SNePS The commands in this section move the user between the SNePSUL
95. e that the negation of the instance of the proposition is to be asserted ii A substitution of the form var term to indicate the appropriate in stance of the proposition where each var is a variable node argument and term is a Lisp object to be converted into a node giving the instance If the attached function returns ni 1 that indicates that neither any instance of the proposition nor any instance of its negation is to be asserted A simple attached function definition for the above example is define attachedfunction sumfn add1 add2 sum cond If all three are numbers check that it is correct and numberp add1 numberp add2 numberp sum if cl cl addl add2 sum snip pos nil snip neg nil If addi and add2 are numbers and sum is a variable es compute the sum and numberp addi numberp add2 sneps isvar n sum snip pos sum cl addl add2 Else don t give an answer t nil 3 The user must attach functions to SNePS predicates using attach function node fun node fun For example attach function Sum sumfn After which the SNePSLOG user can ask questions such as Sum 3 4 7 and Sum 4 6 x and the SNePSUL user can ask questions such as deduce attachedfunction Sum addl 3 add2 4 sum 7 and deduce attachedfunction Sum addl 3 add2 4 sum Sx Chapter 8 SNeBR The SNePS Belief Revision System 8 1 Hypotheses Contexts an
96. eate on find object assert object head propername getr to np end cat art t setr def getf definite to np art np art cat n overlap def t a definite np setr head Find the referent Assume there is exactly one find member deduce member x class build lex getr to np end cat n and disjoint def t overlap mood decl setr head Create a new referent find member assert member hd class build lex getr to np end cat n and disjoint def t overlap mood question setr head x x a variable node to np end nomprop push ps t Return the parse of embedded sentenc sendr embedded t setr head to np end np end pop head t Y A A A E A A A E A E NN A 55 Generation Section ETEFETIERREREFDFIFAPERE respond jump g and getr overlap mood decl say I understand that Canned beginning of echo of statement jump g and getr overlap mood question Answer of question jump g end nullr say I don t know Question not answered g rcall gnp geta agent geta agent Generate the agent as an np reg jump g sub j g subj jump g v geta act say verbize past For this example always use past tense first geta lex geta act g v rcall gnp geta object geta object Generate the object reg to g end to g end
97. eliminated in favor of a global set of minimal nogoods A minimal nogood is a set of hypotheses that is known to be inconsistent such that no subset of it is known to be inconsistent SNeRE 1 Consider the following knowledge base andor 1 1 aheadIs wall aheadIs corridor wff3 andor 1 1 aheadIs corridor aheadIs wall andor 1 1 holding person holding noOne wff6 andor 1 1 holding noOne holding person holding noOne wff5 holding noOne aheadIs corridor wff2 aheadIs corridor Now ask a question whose answer looks like a state constraint holding noOne and aheadIs wall wff9 andor 0 1 holding noOne aheadIs wall lt der w 2 w 3 gt If we now do perform believe aheadIs wall In SNePS 2 7 0 holding noOne will no longer be asserted even though wf f 9 will no longer be asserted once wf f2 is disbelieved This is fixed in SNePS 2 7 1 SNePS 2 7 1 also includes some additional improvements 1 3 System Portability SNePS 2 is written in ANSI COMMON LISP two exceptions are noted in the following paragraph Hence every proper implementation of ANSI COMMON LISP should be sufficient to run SNePS 2 In particular SNePS 2 should run successfully using the following e UNIX operating system e LINUX operating system e Apple Macintosh operating system e Microsoft Windows operating system e Allegro Common Lisp Franz Inc 1 4 COMMANDS AND
98. er Your choice of the UBPL or the alternative licenses if any specified by the Initial Developer in the file described in Exhibit A Exhibit A University at Buffalo Public License 115 Exhibit A University at Buffalo Public License The contents of this file are subject to the University at Buffalo Public License Version 1 0 the License you may not use this file except in compliance with the License You may obtain a copy of the License at http www cse buffalo edu sneps Downloads ubpl pdf Software distributed under the License is distributed on an AS IS basis WITHOUT WARRANTY OF ANY KIND either express or implied See the License for the specific language governing rights and limi tations under the License The Original Code is SNePS 2 7 The Initial Developer of the Original Code is Research Foundation of State University of New York on behalf of University at Buffalo Portions created by the Initial Developer are Copyright C 2007 Research Foundation of State University of New York on behalf of University at Buffalo All Rights Reserved Contributor s NOTE The text of this Exhibit A may differ slightly from the text of the notices in the Source Code files of the Original Code You should use the text of this Exhibit A rather than the text found in the Original Code Source Code for Your Modifications MR Ae MH E HF V NADY ANEIE NAD A A TI Re ZA Ne gt e N a 3 i DR he 10
99. er is empty the arc blocks Otherwise the register is set to the current word and test is performed If the test is non ni 1 the actions are done the front symbol in the input buffer is removed from it if form is present its value is pushed onto the front of the input buffer and parsing continues at state If the test is nil the arc blocks rcall state form test preaction or action register action terminal action If the input buffer is empty the arc blocks Otherwise the register is set to the current word and test is performed If the fest is non nil the preaction or actions are done the current input buffer is saved and replaced by the value of form and parsing continues recursively at state When that level pops back to this one the input buffer is restored as it was saved the value popped to this level is put into register if register 9 3 SYNTAX AND SEMANTICS OF GATN GRAMMARS 83 is x that value replaces the front of the input buffer the actions are done and the terminal action is taken If the test is nil or the recursively called network blocks this arc blocks tst label test action terminal action If the input buffer is empty the arc blocks Otherwise the register is set to the current word and test is performed If the test is non nil the actions are done and the terminal action is taken If the test is nil the arc blocks label is only there so that different t st arcs may be distinguished during traci
100. es you for example to turn on pausing at some interesting point in your demo and to run quickly through all the setup stuff or turn pausing off or enter a Lisp top level somewhere or whatever Here are the commands that allow you to do that These are not SNePSUL commands but they are specially interpreted demo control commands The DC stands for demo control dc pause help dc lisp dc sneps dc snepslog de no pause dc set pause takes an argument e g dc set pause av dc read pause de quit de quit all All commands except dc set pause are atomic They can be given in upper or lower case and they are available in SNePSLOG and the parser as well However the way the parser reads input they have to be followed by a if sentences are terminated that way and dc set pause has to be given as DC SET PAUSE bv because the function parser atn read sentence collects tokens into a list automatically 2 4 2 Writing Altering Source Files For Use With SNePS 2 6 and ACL 6 ACL 6 vs Other Versions of Lisp In any LIspP other than ACL 6 SNePS 2 6 should run like SNePS 2 5 with no noticable differences As described in Section 1 2 ACL 6 differs in the case of its pre defined symbols and input This means that some source files that ran successfully in SNePS 2 5 might not run successfully in SNePS 2 6 using ACL 6 To insure portability across LISP systems any new source files should be written per the advice in the SNeR
101. ewer terms match any given pattern but unlike in Mode 1 variables may not be used as function symbols in queries set mode 3 The knowledge base is cleared and SNePSLOG is put into Mode 3 In this mode the user must specify how terms are represented by using define frame Inference can be more efficient if the user uses a wide variety of frames This mode facilitates path based reasoning and is required if the SNePSLOG version of SNeRE 36 5 is to be used In this mode SNePSLOG syntax may be used to build almost any SNePS network that can be built using SNePSUL 6 3 SNEPSLOG SEMANTICS 61 e show pTermSet Displays the knowledge base in graphical form as a network If pTermSet is omitted all the closed functional terms in the knowledge base are printed If pTermSet is included all but only those terms that match the term patterns in pTermSet are printed Depending on the SNePS installer s choice show either uses dot or JUNG and JIMI dot produces a static figure JUNG JIMI produces a graph that can be manipulated by hand Neither dot JUNG nor JIMI are part of the SNePS distribution dot is part of the Graphviz package which can be downloaded from http graphviz org JUNG and it s associated packages Xerxes Colt and Jakarta Common Collections can be downloaded from http jung sourceforge net JIMI can be downloaded from http Jjava sun com products 3imi The SNePS installer may install either the dot version or the JUN
102. extra level of nesting as shown in the examples below The optional second argument environments defines the places in which the command can legally appear An environment is basically a specification of a location in which a command can be used For example some commands can only be used at the top level some commands can never be used at the top level but only inside some other command some commands can only be used within find commands etc See Section 1 4 for more information on environments environments can either be all to define command as legal in all possible environments or it can be a subset of top rs bns fns ons rearrange specified as a list which will make it legal in the specified environments These abbreviations indicate environments as specified in the following table top The top level of SNePS 2 rs A relation set position embedded in a command bns A node set position in build fns A node set position in find or findassert ons A node set position in any of the other commands rearrange The command is an infix or postfix command A third possibility which is probably the one most commonly used is to supply the name of an already existing command in which case command will be legal in all environments in which the supplied com mand is legal environments defaults to top According to the specified environments defsnepscom automatically updates the SNePSUL variables commands topcommands etc See Section 1 7 By
103. f R x4 in the second use of R etc This can be done by using the macro to create each variable node the first time the restriction occurs and the macro on all subsequent occasions including subsequent rules For example the two rules Every dog is a pet and Every dog hates every cat might be entered as follows assuming that the restrictions Dog a and Cat y have not previously been used in the network assert forall dogl ant build member dogl class dog cq build member dogl class pet assert forall dogl catl s ant build member dogl class dog build member x catl class cat cq build agent dogl act hates object x catl 3 1 3 Recursion Recursive rules such as assert forall x Sy z sant build rel ancestor argl x arg2 xy build rel ancestor argl xy arg2 z cq build rel ancestor argl xx arg2 xz may be used without causing an infinite loop Infinite loops caused by backward chaining on rules such as 3 2 TRACING INFERENCE 25 assert forall x ant build member build fn motherOf fnarg x class duck cq build member x class duck or by forward chaining on rules such as assert forall x ant build member x class number cq build member build fn successor fnarg x class number are terminated under the control of the global parameters depthCutoffBackx and depthCutoffForwardx respectively Ifa subgoal is generated during backward chaining
104. f its arguments in the input line Most commands have an arbitrary number of arguments They are called prefix commands because they can only be entered using Cambridge prefix notation prefix command argument argument Some two argument commands can be entered in infix position and so are called infix commands When an infix command is used in infix position SNePS rearranges the input line to transform the form into a prefix form Precedence is always from left to right An infix command can be used as infix command argument argument or as argument infix command argument with no parentheses Since SNePS always remembers the result of the last top level function an infix command can also be used as infix command argument 6 CHAPTER 1 INTRODUCTION in which case SNePS recalls the result of the last function and makes it the first argument for the infix command before rearranging the form to the prefix notation Similarly some one argument commands can be entered in postfix position and therefore are called postfix commands A postfix command can be used as postfix command argument or as argument postfix command with no parentheses or just as postfix command in which case the result of the last function is used as argument Another kind of one argument command called macro commands have one character names and are used as macro command argument with no parentheses and preferably with no space between the com
105. ffi Lispsymbol Lispstring Lispnumber 1 1 1 i le 58 6 3 CHAPTER 6 SNEPSLOG SNePSLOG Semantics 6 3 1 Semantics of SNePSLOG Commands Here we present a list of the SNePSLOG commands with a description of what they do See Table 6 1 for a concise list of the SNePSLOG commands and their syntax SNePSULcommand Executes the SNePSULcommand and prints the result The default Common Lisp package for symbols in the SVePSULcommand is snepsul LispForm Evaluates the LispForm and prints the result Enters a Lisp read eval print loop To leave the loop type end or activate wff Performs forward inference on all asserted propositions that dominate the wff activate wff Asserts wff and performs forward inference on it and on all asserted propositions that dominate it add to context SNePSLOGsymbol termSet Adds the wffs in termSet as hypotheses in the context named SNePSLOGsymbol ask wff Performs backward inference on wff and prints the inferred positive instances of it askifnot wff Performs backward inference on wff and prints the inferred negative instances of it askwh wff Performs backward inference on wff and prints a list of substitutions which when applied to wff yield asserted wffs askwhnot wff Performs backward inference on wff and prints a list of substitutions which when applied to the negation of wff yield asserted wffs beliefs about pTermSet Returns a set of all the a
106. formation Higher trace levels result in more voluminous trace output For straight forward trace output trace level 4 is suggested For the purposes of printing configurations if a particular field of the configuration is empty it is not printed regardless of the trace level For example if in the configuration being printed there are no registers this field is not printed Note that this does NOT apply to the LEVEL STATE and STRING fields which are always printed regardless of the trace level Trace levels up to level 8 can be utilized with little effort and beneficial effects It is suggested that a novice user closely examine a level 8 trace to become familiar with the operation of the parser 9 3 Syntax and Semantics of GATN Grammars In this section syntactic variables are in italic font optional constituents are enclosed in and J brack ets means zero or more occurrences t means one or more occurrences parentheses and items in typewriter font are object language symbols gatn grammar arc set The contents of a grammar file is a sequence of arc sets arc set state arct Lisp form An arc set is either a list whose car is a state and whose cdr is a list of GATN arcs or it is a list whose car is the symbol and whose cdr is a Lisp form which will be evaluated at grammar load time 82 CHAPTER 9 SNALPS THE SNEPS NATURAL LANGUAGE PROCESSING SYSTEM 9 3 1 Arcs arc The syntax a
107. g pastp The past participle form of a verb that is irregular in this form Used for synthesis The value must be a string plur The plural form of a noun that is irregular in this form Used for synthesis The value must be a string pprt A flag indicating whether the lexeme is a past participle Used for analysis Possible values are t indicating that it is a past participle and nil default indicating that it is not pres The third person singular form of a verb that is irregular in this form Used for synthesis The value must be a string presp The present participle form of a verb that is irregular in this form Used for synthesis The value must be a string presprt A flag indicating whether the lexeme is a present participle Used for analysis Possible values are t indicating that it is a present participle and nil default indicating that it is not tense The tense of a verb Used for analysis Its recognized values are pres indicating present tense default and past indicating past tense root The root stem infinitive or uninflected form of the lexeme Used for analysis and synthesis The value must be a string If omitted will default to the lexeme itself stative Whether or not the verb is stative Used for synthesis Its recognized values are nil indicating not stative default and t indicating stative Any additional features and values may be used in a lexicon if a grammar is written to make use of them
108. group arc is backed into alternate sub arcs are not tried but the entire group fails jump state test action The register is set to the current word and test is performed If test fails the arc blocks Otherwise the actions are done and parsing continues at state pop form test action If test is non nil and the hold register doesn t contain anything put into it on this level the actions are done and the value of form is popped to the level that recursively called this one If this is the top level level 1 then not only must fest be non nil and the hold register not contain anything put into it on this level but also the input buffer must be empty If this is level 1 and the input buffer is not empty or this is any level and test is nil or the hold register contains something put into it at this level this arc blocks push state test preaction action terminal action If the input buffer is empty the arc blocks Otherwise the register is set to the current word and test is performed If the test is non nil the preactions are done and parsing continues recursively at state When that level pops back to this one the value popped to this level is put into the x register that value is pushed onto the front of the the input buffer the actions are done and the terminal action is taken If the test is nil or the recursively called network blocks this arc blocks to state form test action If the input buff
109. h If path P composed with itself one or more times is a path from node x to node y then kplus P isa path from z to y path or path If P is a path from node z to node y or Pa is a path from z to y or or Pn is a path from z to y then or Pi Py P isa path from z to y path and path If P is a path from node x to node y and P is a path from z to y and and P is a path from z to y then and Pi Pp P is a path from z to y path not path If there is no path P from node x to node y then not P isa path from x to y Warning Belief revision will not work for nodes that were inferred via path based inference that used not arcs path relative complement path path If P is a path from node x to node y and there is no path Q from x to y then relative complement P Q isa path from x to y Warning Belief revision will not work for nodes that were inferred via path based inference that used relat ive complement arcs path irreflexive restrict path If P is a path from node z to node y and x y then irreflexive restrict P isa path from x to y path exception path path If P is a path from node zx to node y and there is no path Q from z to y with length less than or equal to the length of P then exception P Q isa path from z to y 2 6 OPERATING ON CONTEXTS 15 path domain restrict pathnode path If P is a path from node x to node y and Q is a path from x to node z
110. h at the end of the loading process and they are all asserted into the current context demo amp optional file pause Reads from the file echoes it and behaves as if that stream had been typed directly into SNePS You can even call demo recursively If file is a string of length 1 that does not name a file or is a symbol whose name is a string of length 1 then a menu of possible demonstrations is printed and the user may pick one of them If file is an integer and the menu lists at least that many demonstrations the one with that number will be run If pause is given its value may be any of t b bv a av or n If pause is t b or bv SNePS will pause before each input command is read If pause is a or av SNePS will pause just after each input is read but before it is executed If pause is omitted or is n SNePS will not pause at all If pause is av or bv a pause message will be printed when the pause occurs otherwise the message will not be printed If both arguments are omitted the menu will be shown and pause defaults to av When SNePS pauses the following commands are available Print this help message Enter Lisp read eval print loop Enter SNePS toplevel loop Enter SNePSLOG Continue without pausing Set pause control Quit this demo Quit all demos any other key Continue the demo os op y vanaonu he5 2 4 USING AUXILIARY FILES 11 All these commands are also available inside demo files This enabl
111. hat logical variables do Variable nodes are created using the macro command The name of a variable node is Vx where x is some number Molecular nodes and pattern nodes have arcs emanating from them Molecular nodes may represent propositions including rules or structured individuals A molecular node that represents a proposition may be asserted or unasserted Pattern nodes represent arbitrary propositions or arbitrary structured individuals and are similar to open sentences in predicate logic Pattern nodes and unasserted molecular nodes are created by the build function Asserted molecular nodes are created by the assert function An unasserted molecular node may be asserted by using the postfix command The name of a pattern node is Px where x is anumber The name of a molecular node is Mx where x is a number The name of an asserted molecular node is printed with a suffix of Once any node is created it may be referred to by its name It is not necessary to include the suffix to refer to an asserted molecular node In fact its use is always interpreted as a call to the command which will assert the node even if it wasn t previously asserted 1 6 CONTEXTS 7 1 6 Contexts A context is a structure with three components 1 a set of hypotheses 2 a set of names 3 a kinconsistent flag The set of hypotheses is a set of nodes which are the assumptions of the context The set of hypotheses is the determining component of t
112. he context in the sense that no two contexts will have the same set of hypotheses The set of names is a set of symbols each of which functions as a name of this context The kinconsistent flag is True if this set of hypotheses is known to be inconsistent A context name intensionally defines a context which is extensionally defined by its set of hypotheses The SNePSUL user always refers to contexts by name and may add assertions to or remove assertions from a context Actually such changes do not change contexts extensionally defined but change the context that the name refers to The system takes care of such details and the SNePSUL user may normally think of a context name as always referring to the same context The user is always working in a particular context called the current context The current context for a particular SNePSUL command may be specified by an optional argument to the command Otherwise all commands are carried out with the default context as current context By default this context is named default defaultct In SNePS 2 3 and later versions a proposition node is not simply asserted or unasserted it is either asserted or unasserted in each context The suffix will be printed with a node s name when that node is asserted in the current context An hypothesis is a node that was asserted by the user using assert or rather than being asserted only because it was derived during inference An hypothesis is always an
113. his initial arc is used to set global variables and other parameters sl cat wh t The only acceptable questions start with a wh word setr subj np setr mood question to vp push np t The only acceptable statements are NP V NP setr subj setr mood decl to vp vp cat v t setr verb x to vp v vp v push np t setr obj x to s final jump s final t The predicate NP is optional s final jump s end overlap embedded t The S might end with an embedded S wrd overlap mood decl to s end wrd overlap mood question to s end 90 CHAPTER 9 SNALPS THE SNEPS NATURAL LANGUAGE PROCESSING SYSTEM jump cat wh push np jump v cat y om erina wrd i JU wena JPP al a ai ad push np jump wrd that Figure 9 1 Graphical version of the example GATN 9 5 EXAMPLES 91 mood subj verb nullr obj s end pop buildq s mood vp v mood subj verb obj obj pop buildq s mood np wrd that t to nomprop An embedded S has that in front of it cat npr t setr np buildgq npr setr def t to np end cat art t setr def getf definite to np art np art cat n t setr np buildq n to np end nomprop push sl t sendr embedded t setr def t setr np to np end np end pop buildq np definite def np t Here is the accompanying lexicon
114. hypothesis of one or more context it may also be asserted in other contexts and might be unasserted in still other contexts Every node is said to be in zero or more contexts A node n is in a context c in any of the following cases e nis one of the hypotheses that define c e nhas been derived from a set of assumptions that is a subset of the set of hypotheses of c e nis dominated by a node in c 1 7 SNePSUL Variables SNePSUL the SNePS User Language has variables which are entirely distinct from SNePS variable nodes The value of a SNePSUL variable is always a set of objects nil if nothing else A SNePSUL variable may be given a value with the or macro commands or with the infix command The value of a SNePSUL variable is obtained by using the macro command SNePSUL variables created and maintained by SNePS are nodes The set of all nodes in the network assertions The set of all nodes in the network that were asserted by the user patterns The set of all pattern nodes in the network varnodes The set of variable nodes in the network relations The set of defined arc labels variables The set of SNePSUL variables defaultct The name of the default context commands The set of SNePSUL commands topcommands The set of commands valid at SNePS top level bnscommands The set of commands valid at node set positions in build type commands fnscommands The set of commands valid at node set positions in find typ
115. inctions between relation unitpath and path since there are places where it matters unitpath relation Any single arc relation is also a unitpath 2S C Shapiro Cables paths and subconscious reasoning in propositional semantic networks In J Sowa Ed Principles of Semantic Networks Explorations in the Representation of Knowledge Morgan Kaufmann San Mateo CA 1991 137 156 14 CHAPTER 2 SNEPSUL COMMANDS unitpath relation If R is a relation from node x to node y then R is a unitpath from y to x path unitpath Any single arc either forward or backward is a path path converse path If P is a path from node z to node y then converse P is a path from y to z path compose path If x1 n are nodes and P is a path from x to 7 41 then compose P P _1 is a path from x to zn Note If the symbol appears between P _ and P then x must be asserted in the current context Examples 1 After doing build member socrates class man thepath compose member class goes from socrates to man but the path compose member class doesn t However af ter doing assert member socrates class man both paths exist 2 find compose xnodes is a way to find all nodes that are asserted in the current context path kstar path If path P composed with itself zero or more times is a path from node x to node y then kstar P is a path from z to y path kplus pat
116. ion of Modifications 02 00 0000 000 109 3 4 Intellectual Property Matters ee ee ee 110 3 3 Required Notices areae s aie eee ed cP eek Sa A 110 3 6 Distribution of Executable Versions o e 110 Bits gt Tsar ger Works s iee ee ist gis A A e 111 4 Inability to Comply Due to Statute or Regulation 2 2 ee 111 5 Application of this License 2 2 0 ee ee 111 6 Versions of the License os secs eae ie eee Be ee Oe Ree Wie we ee 111 6 1 New Versions i A A Be a AD Ge eae a 111 6 2 Effect of New Versions 2 2 ee 111 6 3 Derivative Works sei is eee AO Kd As Bok bP BS e BLA eed 111 642 gt Orgmoft Icesi ess Bia Ge ee A Sl ee Ra ee he ke Se 112 7 DISCLAIMER OF WARRANTY 2 0 2 o e e 112 Sa SPEPMINANON Lu pid ote ie a A Sh whe UR A Ee Bey hh le Bie 112 9 LIMITATION OF LIABILITY 2 202 oe Raha ee oak A Ba Eb a 113 10 U S government end Users es ee e e A 113 dis Miscellaneous o eh dd hs Sd te a a pe Geetha Peed Sng deb ae E 113 12 Responsibility for Claims s ira eee Ae el A Ba Re A Oe ns 114 13 Multiple licensed code oe ee e ee ee 114 Exhibit A University at Buffalo Public License o o e 115 Index 116 Vi CONTENTS List of Figures 4 1 9 1 9 2 10 1 Three ways of associating action nodes with action functions M3 M4 and M5 are act nodes with action nodes SAY M1
117. ion of the GATN parser Depending on the value of trace level defaults to trace level rx see below the configuration is printed in lesser or greater detail 9 2 The Top Level SNaLPS Loop 9 2 1 Input to the SNaLPS Loop Having executed the parse command the user will enter the SNaLPS top level read parse print loop the prompt of which is At each prompt the user may enter any of the following end Terminate the SNaLPS read parse print loop If terminating punctuation flagx see below for a description of SNaLPS variables is non ni 1 then this must be terminated by a terminating punctuation character or Enter an embedded Lisp read eval print loop This is especially useful to set SNaLPS vari ables see below This Lisp loop is left by entering continue or continue or If terminating punctuation flagx see below for a description of SNaLPS variables is non nil then the command for entering an embedded Lisp loop must be terminated by a terminating punc tuation character A sentence A sentence to be parsed is written in the normal fashion as a sequence of words not enclosed in parentheses with punctuation immediately following a word rather than separated by blanks The initial word may be capitalized The sentence may extend over several lines either by ending every line except the last with one or more blanks or by setting the variable terminating punctuation flagx to a list of sentence terminating pu
118. ions using the SNePSLOG Chapter 6 syntax 8 3 Recognizing a Contradiction SNeBR recognizes that the current BS is contradictory when both some proposition pand andor i 0 p are asserted They could be asserted in either order or when the SNeRE mental act be Lieve is about to assert a proposition that contradicts an already asserted proposition Thus SNeBR doesn t recognize that 75 76 CHAPTER 8 SNEBR THE SNEPS BELIEF REVISION SYSTEM the BS is contradictory in the sense that a contradiction could be derived it only recognizes that contradiction has been or is about to be explicitly believed We therefore speak of a BS being known to be contradictory Similarly we speak of a context being known to be contradictory when a contradiction has been derived from a subset of its hypotheses 8 4 Identifying Possible Culprits Every proposition that was ever asserted in any BS will have a support set a set of supports each of which consists of an origin tag and an origin set If the proposition p has been introduced as an hypothesis one of its supports will be hyp p where hyp is the origin tag indicating that p is an hypothesis and p is the origin set containing p as its only element If a proposition p has been derived it will have a support containing an origin tag of either der or ext and an origin set which will be a set of hypotheses from which p was derived The SNePS inference engine SNIP is an implementa
119. is described in Tables 6 1 and 6 2 using the Extended BNF notation Object language terminal symbols are in this font Grouping parentheses are and Alternatives are separated by the character Square brackets and surround optional material The Kleene star indicates zero or more repetitions The Kleene plus indicates one or more repetitions Object language parentheses are and The object language comma is The object language underscore character is _ The symbols 7 j and k are non terminal symbols representing integers Material starting with a semicolon is a comment indicating a restriction on the syntax A SNePSLOG command may continue on subsequent lines as long as the SNePSLOG command couldn t according to the grammar terminate on the initial line If it could and you want to continue the command on a subsequent line end the line with the character A without the quotation marks Note that a SNePSLOGsymbol may be any Lisp symbol string or number If a SNePSLOGsymbol is a symbol it is interned in the snepslog package If a SNePSLOGsymbol is a string it is coerced into a symbol whose symbol name is the original string and is interned in the snepslog package If a SNeP SLOGsymbol is a number it is coerced into a symbol whose symbol name is the Lisp printer representation of the original number and is interned in the snepslog package Two SNePSLOGsymbols are consid ered the same by SNePSLOG if and only if the
120. isbelieved see below and readded the act controlled by whenever will be performed again but the act controlled by when won t perform build action disbelieve objectl build agent Stu state is location here CPU time 0 18 add agent Stu state is location here Hello Stu CPU time 0 02 If an adopted node of the form M if p do a where p is a proposition node and a is an act node is in the network and SNIP back chains into p then a will be performed x describe adopt if build agent who state is location here do build action say objectl Who s object2 here M7 DO M6 ACTION SAY OBJECT1 Who s OBJECT2 here IF M5 AGENT WHO LOCATION HERE STATE IS M7 CPU time 0 19 deduce agent who state is location here Who s here CPU time 0 12 4 2 Primitive Acts The only acts that can actually be performed are primitive acts those whose actions are primitive actions which themselves are associated with primitive action functions Several primitive action functions are predefined The user may define additional ones by using the function define primaction define primaction action relation relationn form This defines action to be a LISP function of arity n whose list of lambda variables is relation relation and whose body is form When the function is called each lambda variable will be bound to a node set
121. itted register at the lower level is set to the value of register at this level If xparse treesx is nil the value of register at the lower level is flattened and a list of one object is changed to just the single object 9 3 4 Terminal Actions terminal action The syntax and semantics of the two terminal actions are to state form The front symbol in the input buffer is removed from it if form is present its value is pushed onto the front of the input buffer and parsing continues at state jump state Parsing continues at state 9 3 5 Forms form The syntax and semantics of GATN forms follows A form consisting of only the symbol x evaluates to the value of the register on the current level buildq fragment form Evaluates to the given fragment list structure with each special symbol replaced as indicated below Replaced by the value of the corresponding form x Replaced by the value of the x register fragment Each fragment is handled as described here and the resulting sublists are spliced together geta unitpath form Returns the set of SNePS nodes at the end of unitpath arcs from all the nodes in the value of form form must evaluate to a set of SNePS nodes getf feature word Looks up word in the lexicon and returns the value of the given feature If word is omitted it defaults to the current word this is only allowed on cat arcs getr register level number Evaluate
122. ks like the smallest of the numbers dbproject nodesetexp relations A virtual relation a set of flat cable sets is created and returned The virtual relation is formed by taking the nodes returned by the SNePSUL node set expression nodesetexp and projecting down the SNePSUL relations included in the sequence relations dbtot nodesetexp Evaluates the SNePSUL nodeset expression nodesetexp which must evaluate to a set of nodes all of whose identifiers look like numbers and returns a node whose identifier looks like the sum of the numbers University at Buffalo Public License UBPD gt Version 1 0 1 Definitions 1 0 1 Commercial Use 1 1 1 2 1 3 1 4 1 5 1 6 1 7 1 8 means distribution or otherwise making the Covered Code available to a third party Contributor means each entity that creates or contributes to the creation of Modifications Contributor Version means the combination of the Original Code prior Modifications used by a Contributor and the Mod ifications made by that particular Contributor Covered Code means the Original Code or Modifications or the combination of the Original Code and Modifications in each case including portions thereof Electronic Distribution Mechanism means a mechanism generally accepted in the software development community for the electronic transfer of data Executable means Covered Code in any form other
123. l or contains only a terminating punctuation character as specified by xterminating punctuation flagx nil otherwise packageless equal form form Equality checker for use in grammars it should be used rather than Lisp s equal It avoids possible pack aging problems associated with the use of Lisp s equal form Any GATN form may be used as a test any non nil value corresponds to True and nil corresponds to False E The always True test 9 3 7 Terminal Symbols The following is a description of Terminal Symbols of the above grammar right hand sides are informal English category Any Lisp symbol used in the lexicon as a lexical category feature Any Lisp symbol used in the lexicon as a lexical feature 86 CHAPTER 9 SNALPS THE SNEPS NATURAL LANGUAGE PROCESSING SYSTEM label Any Lisp symbol register Any Lisp symbol used as the name of a GATN register S expression Any Lisp S expression not otherwise recognizable as a GATN form or test State Any Lisp symbol used to name a GATN state word list word word word Any Lisp string potentially used as a lexicon entry or a word appearing in a sentence 9 4 Morphological Analysis and Synthesis 9 4 1 Syntax of Lexicon Files The syntax of the contents of a lexicon file is lexicon lexical entry lexical entry lexeme feature list A multi sense lexeme is given one feature list for each sense Lexemes on which morphol
124. l objectl x person effect build agent xperson state is location here M36 FORALL V6 ANT P27 CLASS PERSON MEMBER V6 CQ P41 ACT P28 ACTION CALL OBJECT1 V6 EFFECT P38 AGENT V6 LOCATION HERE STATE IS M36 CPU time 0 11 x perform build action call object1 Bill About to do M27 ACTION CALL OBJECT1 BILL I wonder if the act M27 ACTION CALL OBJECT1 BILL has any preconditions The act M27 ACTION CALL OBJECT1 BILL has no preconditions The act M27 ACTION CALL OBJECT1 BILL has a plan M31 ACT M27 ACTION CALL OBJECT1 BILL PLAN M30 ACTION SNSEQUENCE OBJECT1L M23 ACTION SAY OBJECT1 Come here OBJECT2 M17 ACTION SAY OBJECT1 BILL CHAPTER 4 SNERE THE SNEPS RATIONAL ENGINE Intending to do M40 ACTION DO ONE OBJECT1 M30 ACTION SNSEQUENCE OBJECT1 M23 ACTION SAY OBJECT1 Come here OBJECT2 M17 ACTION SAY OBJECT1 BILL Now doing DO ONE M30 ACTION SNSEQUENCE OBJECT1 M23 ACTION SAY OBJEC OBJECT2 17 ACTION SAY OBJEC Come here BILL 1 1 Chose to do the act M30 ACTION SNSEQUENCE
125. lar version of the License You may always continue to use it under the terms of that version You may also choose to use such Covered Code under the terms of any subsequent version of the License published by UB No one other than UB has the right to modify the terms applicable to Covered Code created under this License 6 3 Derivative Works If You create or use a modified version of this License which you may only do in order to apply it to code which is not already Covered Code governed by this License You must a rename Your license so that the phrases University at Buffalo University at BuffaloPL UBPL or any confusingly similar phrase do not appear in your license except to note that your license differs from this License and b otherwise make 1t clear that Your version of the license contains terms which differ from the University at Buffalo Public License Filling in the name of the Initial Developer Original Code or Contributor in the notice described in Exhibit A shall not of themselves be deemed to be modifications of this License 112 UNIVERSITY AT BUFFALO PUBLIC LICENSE UBPL VERSION 1 0 6 4 Origin of License This License is derived from the familiar Mozilla Public License Version 1 1 MPL and differs only in that 1 the title now refers to UB to indicate that UB is the licensor 2 UB retains the sole right to publish revised versions of this license See 6 1 and 6 2 3 Section 6 3 now refe
126. ld action say objectl Hello object2 x else build action say object1l No one s object2 here No one s here CPU time 0 34 describe assert agent Stu state is location here M3 AGENT Stu LOCATION HERE STATE IS M3 CPU time 0 08 describe assert agent Bill state is location here M4 AGENT Bill LOCATION HERE STATE IS M4 CPU time 0 07 perform build action withall vars Sx suchthat build agent x state is location here do build action say objectl Hello object2 xx else build action say object1l No one s object2 here Hello Bill Hello Stu CPU time 0 54 withsome vars suchthat do else where varsis a setof variable nodes suchthat is a proposition with vars free do is an act node with vars free and else is an act node with no free variables withsome finds some substitution for vars for which suchthat is as serted and performs that instance of do If there is no such substitution and else is present it 1s performed x describe deduce agent who state is location here M3 AGENT Stu LOCATION HERE STATE IS M4 AGENT Bill LOCATION HERE STATE IS M3 M4 CPU time 0 19 perform build action withsome vars Sx suchthat build agent x state is location here do build action say objectl Hello object2 x else
127. least i and at most j also satisfy Q1 Qmm nexists i k x1 xn P1 Pnn 01 Qmm abbreviates nexists i k k x1 xn P1 Pnn 01 Qmm nexists _ j _ x1 xn P1 Pnn 01 Qmm denotes the proposition that there are at most k substitution instances obeying UVBR of x1 xn that satisfy P1 Pnn and also Q1 Qmm In all cases are true does not mean the same as are believed Truth is from the point of view of the agent For example if the agent believes p gt q thatis the wff p gt q is asserted in the current belief space and also believes p it still might not believe q if the rule p gt q hasn t fired but when the rule p gt q does fire the agent will believe q That is when the agent reasons using p gt q it will conclude q 6 4 SNIP in SNePSLOG 6 4 1 Rules of Inference The implemented rules of inference of SNePSLOG are listed in this section Not all logically possible op erations have been implemented Each connective and quantifier could have introduction rules and elim ination rules Each of those could operate while forward chaining while backward chaining and during bi directional inference There are two aspects to bi directional inference 1 If a proposition forward chains into one antecedent of a rule backward chaining is performed on the other antecedents and fir
128. lt past Past tense Alternative p future Future tense Alternatives futr ftr fut number The number of the verb group Possible values are sing Singular Default Alternative singular plur Plural Alternatives p1 plural person The person of the verb group Possible values are pl First person Alternatives firstperson personl p2 Second person Alternatives secondperson person2 p3 Third person Default mode Whether the verb group is in a declarative interrogative etc sentence Possible values are decl Declarative Default int Interrogative Alternatives yng interrogative interrog ques question intrg O imp Imperative Alternatives imper imperative impr command request req inf Infinitive Alternatives infinitive infin root gnd Gerundive Alternatives gerund ing grnd progressive aspect Whether the verb group is progressive Possible values are non prog Non progressive Default prog Progressive Alternatives progr prgr prg progress progressive A lexeme with stative t inits lexical feature list will not be put into progressive form perfective aspect Whether the verb group is perfective Possible values are non per f Non perfective Default perf Perfective Alternatives pft prft prfct perfect perfective 9 5 EXAMPLES 89 voice The voice of the verb group Possible values are active Active voice Default pass Passive voice Alternative passive q
129. mand and the argument Before passing it to the evaluator the SNePS reader expands this form to a standard Cambridge prefix form 1 5 Types of Nodes There are four types of nodes in the SNePS network base variable molecular and pattern Base nodes are distinguished by having no arcs emanating from them A base node may be created by the user s referring to it by name in the proper context In such a case the name of a base node can be any Lisp symbol If a number is used the node s name is a symbol whose symbol name is a string of the characters that makes up the number If a string is used the node s name is the symbol whose symbol name is that string A base node may also be created using the macro command in which case the node s name is Bx where x is some integer A base node is assumed to represent some entity individual object class property etc It is assumed that no two base nodes represent the same identical entity One may of course introduce an equality or equivalence relation and the rules for using them In that case the introduced equality or equivalence relation is weaker than the identity relation just referred to This is the most basic way that SNePS assumes an intensional representation no two nodes are intensionally identical even though they might be extensionally equivalent Variable nodes also have no arcs emanating from them but represent arbitrary individuals or propositions in much the same way t
130. mented in Chapter 4 has a SNeRE version predefined in Mode 3 These are listed below Policies Policies connect propositions to acts and must be adopted in order to operate as described ifdo p a If SNIP backchains into p perform a whendo p a If SNIP forward chains into p perform a and then unadopt the whendo wheneverdo p a If SNIP forward chains into p perform a Mental Acts adopt p The policy p is adopted and forward chaining is performed on it That is if the system is ready to act in accord with p it does so believe p First the following special cases of belief revision are performed e Ifandor 0 0 p is believed itis disbelieved e Ifandor i 1 p q 4is believed and q is believed then q is disbelieved Then p is asserted and forward inference is performed on it disbelieve p The proposition p which must be a hypothesis is unasserted unadopt p The policy p is unadopted Control Acts achieve p Ifthe proposition p is asserted do nothing Otherwise use deduce to infer plans for bringing about the proposition p and then do one of them do all al an Perform all the acts al aninanondeterministic order do one al an Nondeterministically choose one of the acts al an and perform it snif if pl al if pn an else da Using deduce determine which of the pi hold If any do nondeterministically choose one of them say pj and perform aj
131. mitted prints the information on defaultct list hypotheses context name Returns the hypothesis set of the context named context name If context name is omitted returns the hy pothesis set of xdefaultct 2 7 Building Networks The commands of this section add information to the network either in the form of a node a node and some arcs or an assertion tag It is not possible to add just an arc to the network Isolated nodes cannot be added to the network so the commands and can only be used within the lexical context of a build assert or add 16 CHAPTER 2 SNEPSUL COMMANDS We will use the term wire to mean a labelled arc and the node it points to So a molecular node has a set of wires coming out of it adopt relation nodeset Builds and adopts the specified policy See Chapter 4 As of SNePS 2 7 1 the SNePSUL command adopt is just a synonym of assert see below build relation nodeset assert relation nodeset context specifier add relation nodeset context specifier Puts a node in the network with an arc labelled relation to each node in the following nodeset and returns a singleton set containing the built node The new node is added to the value of the SNePSUL variable nodes If this new node would look exactly like an already existing node i e would have exactly the same set of wires emanating from it then no node is built but a singleton set containing the extant node is returned build c
132. n the order given is appended to the end of register If xparse treesx is nil the value of register is then flattened and a list of one object is changed to just the single object hold category form The value of form is put into the hold register as an entry whose category is the value of category liftr register form register is set to the value of form on the next higher level If form is omitted register at that level is set to the value of register at this level If xparse treesx is nil the value of register at the higher level is flattened and a list of one object is changed to just the single object setr register form register is set to the value of form If more than one form appears register is set to a list of their values in the order given If xparse treesx is nil the value of register is then flattened and a list of one object is changed to just the single object S expression Any Lisp S expression not otherwise listed as a GATN action or form evaluates normally 84 CHAPTER 9 SNALPS THE SNEPS NATURAL LANGUAGE PROCESSING SYSTEM 9 3 3 Preactions preaction or action preaction action A preaction or action is either a preaction or an action preaction sendr register form The only preaction is sendr sendr sets the value of register on the level about to be called to the value of form If more than one form appears register is set to a list of their values in the order given If form is om
133. nctuation marks See below for a description of SNaLPS variables Each punctuation mark must be parsed by the grammar as a separate word Besides the grammar the behavior of SNaLPS is affected by a set of variables and by the trace level These are described below 9 2 2 SNaLPS Variables These variables affect the behavior of SNaLPS and may be set within an embedded Lisp loop within the SNaLPS loop xall parsesx If nil default only the first parse is produced if t all parses are produced with the user queried as to whether or not to produce more parses after each one is printed parse trees x If nil default the contents of all GATN registers are assumed to be sequences flat tened lists any list structure stored into a register is automatically flattened and an atom and a list of one atom are considered to be the same if t this flattening is not done and list structure is preserved terminating punctuation flagx A list of punctuation marks that will be used to signal the end of a sentence Each punctuation mark should be enclosed in quote marks to make it a string E g the list might be 2 If this variable is nil default the only way to let a sentence extend over more than one line is to end each line before the last with a blank If this variable is non nil the SNaLPS top level reader will continue to read words from successive lines until a terminating punctuation mark is encountered 9 3 SYNTAX AND SEM
134. nd semantics of GATN arcs follows Except as noted all forms and actions on an arc are evaluated in left to right order in the environment of the arc that they are on cat category test action terminal action If the input buffer is empty the arc blocks The lex register is set to the current word If any sense s of the word have the given lexical category in the lexicon the arc is taken non deterministically for each sense For each sense x is set to the root of the word sense and test is performed If test fails the arc blocks for the given word sense otherwise actions are done and the terminal action is taken call state form test preaction or action register action terminal action If the input buffer is empty the arc blocks The register is set to the current word and test is performed If test fails the arc blocks Otherwise the preaction or actions are done the value of form replaces the current word in the input buffer if the value of form is a list it is spliced in and parsing continues recursively at State If the recursively called network blocks this arc blocks also When that level pops back to this one the value popped to this level is put into register the value of the register is pushed onto the front of the input buffer the actions are done and the terminal action is taken group arc Presumably at most one arc has a test that succeeds That arc is taken group is provided for efficiency only If a
135. ng vir category test action terminal action If the fest is non nil and the hold register contains an entry of the given category put into it at this or a higher level then the arc is taken non deterministically for each such entry The entry taken is removed from the hold register put into the x register and pushed onto the front of the input buffer Then the actions are done and the terminal action is taken If the test is nil or hold contains no appropriate entry this arc blocks wrd word list test action terminal action If the input buffer is empty the arc blocks Otherwise the current word is compared to word list If it is among the words in word list the x register is set to the current word and test is performed If the test is non nil the actions are done and the terminal action is taken If either the test is nil or the current word is not on the word list the arc blocks 9 3 2 Actions action The syntax and semantics of GATN actions follows addl register form The value of form is appended to the front left end of register If more than one form appears a list of their values in the order given is appended to the front of register If xparse treesx is nil the value of register is then flattened and a list of one object is changed to just the single object addr register form The value of form is appended to the end right end of register If more than one form appears a list of their values i
136. nt Licensable by grantor 1 11 Source Code means the preferred form of the Covered Code for making modifications to it including all modules it contains plus any associated interface definition files scripts used to control compilation and in stallation of an Executable or source code differential comparisons against either the Original Code or another well known available Covered Code of the Contributor s choice The Source Code can be in a compressed or archival form provided the appropriate decompression or de archiving software is widely available for no charge 1 12 You or Your means an individual or a legal entity exercising rights under and complying with all of the terms of this License or a future version of this License issued under Section 6 1 For legal entities You includes any entity which controls is controlled by or is under common control with You For purposes of this definition control means a the power direct or indirect to cause the direction or management of such entity whether by contract or otherwise or b ownership of more than fifty percent 50 of the outstanding shares or beneficial ownership of such entity 2 Source Code License 2 1 The Initial Developer Grant The Initial Developer hereby grants You a world wide royalty free non exclusive license subject to third party intellectual property claims a under intellectual property rights other than patent
137. o new OBJECT2 V1 WHEN P2 AGENT V1 LOCATION HERE STATE IS M3 CPU time 0 19 4 3 ASSOCIATING PRIMITIVE ACTION NODES WITH THEIR FUNCTIONS 35 perform build action withall vars x suchthat build min 2 max 2 arg build member x class person build agent x state is location here do build action say object1l Hello old object2 x Hello old Stu CPU time 0 60 clear infer Node activation cleared Some register information retained CPU time 0 01 add agent Bill state is location here Hello new Bill CPU time 0 31 The clear infer was needed to mark the change in time and discourse context 4 3 Associating Primitive Action Nodes with Their Functions SNeRE will recognize an act node by its action arc to another node However if that latter node repre sents a primitive action it must be associated with a primitive action function To provide flexibility in the representation of primitive actions the user is obliged to explicitly associate primitive action nodes with their functions attach primaction action node form action function name action node form must be a SNePSUL form that evaluates to a node n or a singleton nodeset n and act ion function name must be a symbol that was defined to name a primitive action function f These are associated with each other so that when an act node whose action node is n is perf
138. ogical synthesis is to be done for generation must have only one feature list feature list feature pair feature pair feature value lexeme Any Lisp string feature Any Lisp atom but see below for standard features value Any readable Lisp object but see below for standard values Standard Lexical Features and Values The following lexical features will be recognized by GATN arcs and or the morphological analyzer synthe sizer With each feature it is shown whether it is used for morphological analysis or synthesis or both A feature whose value is the default for that feature may be omitted from the feature list ctgy The lexical category of the entry Used for analysis The following values of this feature are recog nized by the morphological analyzer synthesizer adj An adjective n A common noun v A verb multi start This lexeme is the first word of a multi word lexeme See the feature mult i rest described below multi rest A list of the rest of the words after this one that form a multi word lexeme Each word in the list must be a string There must also be a lexical entry for the multi word lexeme as a whole num The number of a noun or verb Used for analysis Its recognized values are sing for singular default and plur for plural 9 4 MORPHOLOGICAL ANALYSIS AND SYNTHESIS 87 past The past tense form of a verb that is irregular in this form Used for synthesis The value must be a strin
139. oms 53 unev trace 25 unin trace 26 Unique Variable Binding Rule 64 unitpath 12 13 unlabeled 61 untrace 6l cl user use gui shows 3 61 variables SNEPSUL 7 variables INDEX SNEPSUL variable 7 varnodes SNEPSUL variable 7 verbize 88 wff 62 63 wffCommand 61 62 wffName 61 unassigned 62 wffNameCommand 61 when do 27 whendo 67 whenever do 27 wheneverdo 67 with snepsul standard eval 48 with snepsul toplevel styl val 48 with snepsul trac val 48 withall 32 67 withsome 33 68 wordize 89 119
140. on ceptualized function or relation in the domain Recall that what is considered a truth functional relation in standard logic is a proposition valued function in SNePS Function symbols do not have fixed arity in SNeP SLOG In fact p a b c implies p a b by reduction inference The user must be careful to always give a function symbol the same number of arguments if this behavior is not wanted A functional term consisting of a function symbol and its arguments denotes a mental entity possibly a proposition in the domain namely that mental entity that results from applying the denotation of the functional term to the denotations of the arguments No two functional terms that are syntactically different denote the same mental entity This is called the Uniqueness Principle Functional terms with sets as arguments takes the elements of the sets conjunctively For example motherof John Jane Tom Betty is the mother of John Jane Tom and Betty Also Isa Rover Fido dog pet is the proposition that Rover and Fido are both dogs and pets andor i j P1 Pn denotes the proposition that at least i and at most j of P1 Pn are true P1 and and Pn isan abbreviation of andor n n P1 Pn P1 or or Pnisan abbreviation of andor 1 n P1 Pn and P1 Pn isan abbreviation of andor n n P1 Pn or P1 Pn isan abbreviation of andor 1 n P1 Pn nand P1 Pn isan abbreviation of
141. on supplier s SUPPLIER RELATION S S1 SNAME SMITH STATUS SUPPLIER RELATION S S2 SNAME JONES STATUS SUPPLIER RELATION S S3 SNAME BLAKE STATUS SUPPLIER RELATION S S4 SNAME CLARK STATUS SUPPLIER RELATION S S5 SNAME ADAMS STATUS CPU time 0 12 sname status cit 2 1 30 20 30 0 0 GII GIJ CIJI CIT lY lY TY pS LON PAR PAR LON CI Y ATHE Virtual relations are created without building any new SNePS network structure To make these relations permanent use the SNePSUL dbAssert Virtual function For example to create a CITYSTATUS re lation that is a projection of the SUPPLIER relation down the CITY and STATUS attributes we would first define CITYSTATUS as a new SNePS relation CITYSTATUS CPU time 0 Then we would do define citystatus 03 describe dbAssertVirtual dbproject citystatus relation M13 CITY LONDON CITYSTATUS RELATION STATUS M14 CITY PARIS CITYSTATUS RELATION STATUS 1 M15 CITY PARIS CITYSTATUS RELATION STATUS 3 M16 CITY ATHENS CITYSTATUS RELATION STATUS M13 M14 M15 M16 CPU time 0 28 find supplier relation city status 104 CHAPTER 10 SNEPS AS A DATABASE MANAGEMENT SYSTEM 10 1 2 Select Select is an operation that is given a relation and specific values
142. ormed f is applied to the nodesets at the end of the arcs whose relations are the lambda variables of f As an example we will establish three primitive actions using a variety of representation schemes gt define primaction sayfun objectl object2 Print the the argument nodes in order followed by a period format t amp A A first ns to lisp list objectl first ns to lisp list object2 SAYFUN gt define primaction exclaimfun objectl object2 Print the the argument nodes in order followed by an exclamation mark format t TTEA A 75 first ns to lisp list object1 first ns to lisp list object2 EXCLAIMFUN gt define primaction questionfun np vp Print the the argument nodes in order followed by a question mark format t amp A A TS first ns to lisp list np 36 CHAPTER 4 SNERE THE SNEPS RATIONAL ENGINE first ns to lisp list vp QUESTIONFUN gt attach primaction say sayfun build lex exclaim exclaim exclaimfun find entity assert entity Af question expression question questionfun gt CPU time 0 17 x perform build action say objectl Hello object2 stu Hello Stu CPU time 0 18 x perform build action xexclaim objectl Hello object2 Bill Hello Bill CPU time 0 14 perform build action question np Who s vp there Who s there CPU time 0 16 Fig
143. preter does no printing and returns whatever the SNePSLOG command would return 70 CHAPTER 6 SNEPSLOG parser nl tell string Assuming that a lexicon and GATN grammar have been loaded passes st ring to the parser and returns a string containing whatever the parser returns May be called without a package qualifier from the snepslog package snepslog ask string amp key verbose snepslog askifnot string amp key verbose snepslog askwh string amp key verbose snepslog askwhnot string amp key verbose string must be a valid argument to the SNePSLOG command ask askifnot askwh or askwhnot respectively These functions all give string to the appropriate SNePSLOG command and return what the SNePSUL command would print If verbose is t the functions print the results as well as returning them 6 7 The Java SNePS API Users who are either running SNePS under Franz s ACL or as the distributed executable have a Java SNePS API available To use SNePS from a Java program e include in the Java program the import statements import java util HashSet import edu buffalo sneps JavaSnepsAPI import edu buffalo sneps Substitution e include the locations of the files jlinker jar and JavaSnepsAPI jar in the Java classpath The Java program must create an object of the JavaSnepsAPLI class There are two constructors for JavaSnepsAPI l public JavaSnepsAPI java lang String config_file int interface_port Crea
144. r package for use in grammars but it is seldom necessary for a grammar writer to call this function directly because it is called automatically on appropriate GATN arcs Nevertheless when writing a lexicon and grammar it is useful to use this function to test the lexicon and see which forms englex lookup considers to be regular 88 CHAPTER 9 SNALPS THE SNEPS NATURAL LANGUAGE PROCESSING SYSTEM 9 4 3 Functions for Morphological Synthesis The two functions in this section should be used in appropriate places on arcs of GATN generation grammars in order to produce correct surface forms of nouns and verb groups They both require a loaded lexicon for irregular words but will assume a word is inflected regularly if it doesn t have a lexical entry It s a good idea to try them out from a top level Lisp listener as they are surprisingly powerful verbize tense number person mode progressive aspect perfective aspect voice quality modal lexeme Returns a list of strings which forms a verb group lexeme should be a string but a symbol or node will be coerced into a string and is presumed to be the root infinitive form of a verb All the other parameters are optional If modals appear they must immediately precede lexeme The other parameters may appear in any order The possible values and the default values of the optional parameters are tense The tense of the verb group Possible values are pres Present tense Defau
145. r the meaning of the various pause controls t b bv a av and n see page 10 e describe context SNePSLOGsymbol Lists the details of the context named SNePSLOGsymbol If SNePSLOGsymbol is omitted describes the default context e describe terms pTermSet This is only useful in Mode 3 If pTermSet is omitted all the closed functional terms in the knowledge base are described If pTermSet is included all but only those closed functional terms that match the term patterns in pTermSet are described The description of an individual constant is itself The de scription of a functional term is formed from the LispString included when the frame is defined for the term s function symbol The description is the LispString with every instance of rel replaced by the description of the filler s of the re1 slot For example after the frame definitions define frame mother nil motherof the mother of motherof define frame female property object object is property the description of female mother Betty will be The mother of Betty is female e expert Turns on the expert mode in which the wffName of listed terms is shown as in normal mode cf and in addition when a SNePSLOG proposition is printed its support set is shown e lisp Leaves the SNePSLOG loop and returns to the Lisp listener e list asserted wffs SNePSLOGsymbol Lists all propositions that are asserted in the context name
146. rammars etc and that other common uses such as for functions are available too Ssymbol A macro command that creates a new variable node assigns a singleton set containing the new node as the value of the SNePSUL variable symbol and returns that set This may not be used at the top level SNePSUL loop since that would create an isolated node 2 8 Deleting Information The commands of this section delete information from the network and are mainly intended for use after mistakes or when debugging erase nodeset silent erase nodeset 2 9 FUNCTIONS RETURNING SETS OF NODES OR OF UNITPATHS 17 Removes all nodes in all nodesets from the network along with any nodes that become isolated in the process that is all nodes which no longer have any arcs connected to them and all nodes that were dominated by nodes it erases that are not also dominated by other nodes Refuses to delete nodes that have arcs coming into them silent erase is like erase but does no printing resetnet reset relations Reinitializes the network to the state in which no nodes have been built If reset relations is t the set of SNePS relations is reset to the pre defined ones If reset relations is nil default the defines relations and declared path based inference rules remain as is clear infer all Deletes any information placed in the active connection graph version of the network by SNIP I e all deduction
147. randomlyx nth random cardinality ns objectl ns to lisp list objectl first ns to lisp list objectl but the user could redefine it if she wanted to make a more intelligent decision about which act should be performed 46 CHAPTER 4 SNERE THE SNEPS RATIONAL ENGINE Chapter 5 Program Interface 5 1 Transformers These functions convert Lisp objects to SNePS nodes and vice versa apply function to ns fnns Converts the node set ns to a list of lisp objects applies the function fn to that list then converts the result to anode set and returns that lisp list to ns list Returns a set of nodes whose identifiers look like the printed representations of the objects in the list list ns to lisp list ns Returns a list of Lisp objects corresponding to the SNePS nodes in the node set ns node to lisp object nde Returns a Lisp object corresponding to the SNePS node nde The Lisp object will be either a number or a symbol lisp object to node obj Returns a SNePS node whose identifier looks like obj 5 2 With SNePSUL Reader Macro The with snepsul reader macro is provided so that users can easily incorporate calls to SNePSUL commands within Lisp code fi snepsul form snepsul form The form following is taken to be a list of SNePSUL forms each of which will be executed just as if it had been typed that way at the SNePS prompt regardless of the package in which the i
148. re of the breach All subli censes to the Covered Code which are properly granted shall survive any termination of this License Provisions which by their nature must remain in effect beyond the termination of this License shall survive 8 2 If You initiate litigation by asserting a patent infringement claim excluding declatory judgment ac tions against Initial Developer or a Contributor the Initial Developer or Contributor against whom You file such action is referred to as Participant alleging that a such Participant s Contributor Version directly or indirectly infringes any patent then any and all rights granted by such Participant to You under Sections 2 1 and or 2 2 of this License shall upon 60 days notice from Participant terminate prospectively unless if within 60 days after receipt of notice You either 1 agree in writing to pay Participant a mutually agreeable reasonable royalty for Your past and future use of Modifications made by such Participant or 11 withdraw Your litigation claim with respect to the Contributor Version against such Participant If within 60 days of notice a reasonable royalty and payment arrangement are not mutually agreed upon in writing by the parties or the litigation claim is not withdrawn the rights granted by Participant to You under Sections 2 1 and or 2 2 automatically terminate at the expiration of the 60 day notice period specified above b any software hardware or device other
149. reates an unasserted node assert is just like build but creates the node as an asserted node an hypothesis and adds it to the hypothesis set of the context specified by context specifier add is just like assert but in addition triggers forward inference Note where relation is specified in the syntax neither a converse relation nor a non unit path is allowed build is not a top level SNePSUL command in SNePS 2 activate nodeset context specifier Finds all the nodes that dominate the nodes in nodeset including the nodes in nodeset themselves and that are asserted in the context specified by context specifier and triggers forward inference on them node context specifier A postfix command that asserts node in the context specified by context specifier and returns a singleton set containing node assert context specifier isequivalentto build context specifier build is equivalent to assert symbol A macro command that creates a new base node assigns a singleton set containing the new node as the value of the SNePSUL variable symbol and returns that set This may not be used at the top level SNePSUL loop since that would create an isolated node Note The macro is smart enough to guess whether you want to create a base node or whether the standard COMMON LISP dispatching macro is intended This means that the with snepsul syntax is available at the SNePS top level as well as in GATN g
150. rived belief of the BS defined by that context that needed the hypothesis for its derivation is automatically no longer asserted in the BS 8 7 Warning About a Belief Space Known to be Contradictory Every set of possible culprits a is a set of hypotheses known to be contradictory called a nogood A minimal nogood is a nogood such that no subset of it is known to be contradictory A global set of minimal nogoods is maintained Any attempt to create a context that is a superset of any minimal nogood is an attempt to create a context already known to be contradictory and the user is warned about it 78 CHAPTER 8 SNEBR THE SNEPS BELIEF REVISION SYSTEM Chapter 9 SNaLPS The SNePS Natural Language Processing System The SNePS Natural Language Processing System consists of a Generalized Augmented Transition Network GATN grammar interpreter compiler and a morphological analyzer synthesizer 9 1 Top Level SNaLPS Functions The top level SNaLPS functions are not SNePSUL commands so to use them from the top level SNePSUL loop use the command atnin file amp key check syntax Loads the GATN grammar in file See Section 9 3 for the syntax of the grammar file If check syntax is t syntax checks and some simple semantic checks are performed on the grammar as it is input It reports errors such as undefined target states pre actions after actions on push arcs and syntactically ill formed arcs If check syntax is nil default
151. rs to the phrases University at Buffalo University at BuffaloPL UBPL 4 the License shall be governed by law provisions of the state of New York and any litigation relating to this License shall be subject to the jurisdiction of the state and federal courts of the State of New York and all parties consent to the exclusive personal jurisdiction of those courts See 11 and 5 Research Foundation of State University of New York on behalf of University at Buffalo is cited as the copyright owner of the original code See Exhibit A 7 DISCLAIMER OF WARRANTY COVERED CODE IS PROVIDED UNDER THIS LICENSE ON AN AS IS BASIS WITHOUT WAR RANTY OF ANY KIND EITHER EXPRESSED OR IMPLIED INCLUDING WITHOUT LIMITATION WARRANTIES THAT THE COVERED CODE IS FREE OF DEFECTS MERCHANTABLE FIT FOR A PARTICULAR PURPOSE OR NON INFRINGING THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE COVERED CODE IS WITH YOU SHOULD ANY COVERED CODE PROVE DEFECTIVE IN ANY RESPECT YOU NOT THE INITIAL DEVELOPER OR ANY OTHER CONTRIB UTOR ASSUME THE COST OF ANY NECESSARY SERVICING REPAIR OR CORRECTION THIS DISCLAIMER OF WARRANTY CONSTITUTES AN ESSENTIAL PART OF THIS LICENSE NO USE OF ANY COVERED CODE IS AUTHORIZED HEREUNDER EXCEPT UNDER THIS DISCLAIMER 8 Termination 8 1 This License and the rights granted hereunder will terminate automatically if You fail to comply with terms herein and fail to cure such breach within 30 days of becoming awa
152. rules are returned to their unactivated state as if no inference had yet been performed It is recom mended that clear infer be used instead See below clear infer Like clear infer al1l but retains some pointers from rules to their instances that makes node based in ference faster clear infer is recommended over clear infer all unless there is a specific reason to use the latter 2 9 Functions Returning Sets of Nodes or of Unitpaths The functions described in this section neither add to nor delete from the network Rather they compute and return sets either of nodes or of unitpaths node A list of nodes at the top level of the SNePSUL loop or in a context where a node set is required is treated as an expression whose value is a set of the nodes in the list x symbol A macro command function which returns the set of nodes in the value of the SNePSUL variable symbol list nodes context name Returns the set of all nodes that are in the context named context name If context name is omitted returns the set of all nodes that are in defaultct S expression The set of nodes obtained by evaluating the Lisp S expression amp nodeset Infix function that returns the intersection of the nodesets nodeset Infix function that returns the union of the nodesets nodeset nodeset Infix function that returns the set of nodes in the first nodeset but not in the second nodeset nodeset symbol
153. s legal as long as the runtime values of com represent proper SNePSUL commands commands is not Supplying an optional digit argument can be used to select a specific evaluation function or to suppress output arg eval function silent syntax no arg topsneval no EN 1 topsneval no TN ee Se 2 eval no o 3 topsneval yes Oils ia 4 eval yes Asi For example 4 build relation node will use the function eval to evaluate the form hence build can be used and will suppress any output generated by the snepsul command 5 2 1 Controlling the Evaluation of SNePSUL Forms Generated by defvar with snepsul eval functionx with snepsul standard eval The value of this variable has to be a function of two arguments an eval function and a form to which the function should be applied Binding this variable to different functions can implement various different evaluation behaviors such as normal evaluation tracing top level like echoing evaluating and printing the result etc when the form gets evaluated inside with snepsul eval The following evaluation functions are available with snepsul standard eval function form Standard function used by with snepsul eval to evaluate form with evaluation function with snepsul trac val function form Does not actually evaluate form only prints it for debugging purposes with snepsul toplevel styl val fun
154. s to SNePS via the tell ask interface see 86 7 The final cases in which the inference system could get into infinite recursion have been eliminated see 3 1 3 and 86 4 2 The SNePSUL intext command and the new SNePSLOG load command silently load files Pre viously one could not have a node named with a string the Lisp reader could not handle such as and node to lisp object did not handle nodes with names like 5 pounds Both these problems are now fixed A node s name can now be any Lisp number string or symbol and node to lisp object is able to handle it Previously inference and acting tracing was on by default now they are off by default Previously in add to context only the wffs in termSet that had already been introduced to the KB as hypotheses were added as hypotheses of the context Now all the wffs listed as to be added are asserted into the context An procedural attachment facility has been added It is described in Chapter 7 The guarded acts in snif and sniterate can now take sets of conditions and sets of acts snif and each loop of sniterate still performs one act one of whose guards holds The mental acts adopt and unadopt are now available for adopting and unadopting policies and should be used for those purposes instead of be lieve and disbelieve A chapter on SNeBR Chapter 8 has been added XGinseng has been eliminated because it was implemented in Garnet which has not been maintained and was difficult to use SNeP
155. s to the value of the given register If level number is present the value of register on the given level 1s returned The top level of the network is level number 1 and each push increments the level number by 1 If level number is present it must be a higher level smaller integer than the current level 9 3 SYNTAX AND SEMANTICS OF GATN GRAMMARS 85 lex Evaluates to the current word first symbol on the input buffer as it appears before any morphological analysis has been done nullr form Evaluates to non ni1 if the form evaluates to nil and to nil otherwise overlap form form Evaluates to the set intersection of the values of the two forms For this purpose an atom is treated as a singleton set containing itself register A form consisting of only the name of a register evaluates to the value of that register on the current level If the register has never been given a value on the current level its value is nil S expression Any Lisp S expression not otherwise listed as a GATN form evaluates to its Lisp value Within a form to be evaluated by Lisp buildq geta getf getr nullr and overlap are all Lisp functions that operate as described in this Subsection 9 3 6 Tests A test fails if it evaluates to nil and succeeds otherwise test The syntax and semantics of GATN tests are disjoint form form Evaluates to not overlap form form endofsentence Returns t iff the current input buffer is empty ni
156. scard some of the possible culprits from the context choosing them as actual culprits e examine all the hypotheses in the context even those that are not possible culprits e see the culprits already chosen e get instructions e or quit revising this set of possible culprits This choice repeats until the user chooses to quit revising this set This entire process is repeated for each set of possible culprits until at least one actual culprit has been chosen for each one After identifying at least one actual culprit in each set of possible culprits the user is shown the remaining list of consistent really not known to be inconsistent hypotheses in the context and given the same choices as though it were a list of possible culprits Thereby the user may disbelieve additional hypotheses even though they were not responsible for the contradiction Finally the user is given a chance to add new hypotheses to the context This is especially useful if a rewording of one of the culprits is wanted 8 6 Disbelief Propagation Every culprit chosen either automatically or by the user is removed from the set of hypotheses of the current context The assertion status of a proposition is computed dynamically whenever it is needed A proposition is asserted in a given BS if the origin set of at least one of its supports is a subset of the hypothess set of the context of the BS Thus if an hypothesis is removed from a context any de
157. scribed by pTermSet 5 The function nl tell string has been added It may be called from the top level Lisp listener in the snepslog package Assuming that a lexicon and GATN grammar have been loaded nl te11 passes string to the parser and returns a string containing whatever the parser returns 6 Linebreaks may occur in input to the SNePSLOG reader but if the input looks like it might end prematurely the character A without the quotation marks may be placed before the end of line to indicate that the input continues on the next line For example the wff p gt q may end just before the gt if and only if the line with p ends with a A This is also very useful in a define frame command if you want to put the documentation string on the next line Previously this was an undocumented feature but was used instead of which caused problems if a linebreak occurred in a path just after a converse relation like subclass 4 CHAPTER 1 INTRODUCTION 7 SNePSLOG now allows the use of and or nand nor xor and if f each followed by a termSet For example all x Entity x gt xorfAnimal x Vegetable x Mineral x 1 In certain circumstances activate caused an error That has been fixed 2 See the note about amp gt Introduction under SNePSLOG above 3 See the note about v gt Introduction under SNePSLOG above SNeBR Restriction sets in node supports and in contexts have been
158. sitions that were introduced to the system agent without justification typically by the user using a wff Command terminated by or At any time one context is the current context The agent believes all the propositions that are hypotheses in the current context as well as all propositions that have been derived from those hypotheses The set of hypotheses and derived propositions that are currently believed is called the current belief space Every currently believed proposition is also called asserted and when its wffName is printed it is terminated by an Every well formed expression of SNePSLOG is a term of the language Expressions that look to the logically trained user like formulas or sentences are actually proposition valued terms even though SNeP SLOG uses wf fi for its wffName The significance of this is that because terms may be arguments of terms and may have variables ranging over them without leaving first order logic metapropositions propo sitions about propositions are allowed in SNePSLOG Given that background we will now discuss the intended semantics of SNePSLOG expressions An individual constant expressed in SNePSLOG as a Lisp symbol string or number denotes a mental entity The unique names assumption holds meaning that no two mental entities may be considered to be entirely equal A function symbol also expressed in SNePSLOG as a Lisp symbol string or number denotes a c
159. spect to disputes in which at least one party is a citizen of or an entity chartered or registered to do business in the United States of America any litigation relating to this License shall be subject to the jurisdiction of the state and federal courts of the State of New York and all parties consent to the exclusive personal jurisdiction of those courts with the losing party responsible for costs including without limitation court costs and reasonable attorneys fees and expenses The application of the United Nations Convention on Contracts for the International Sale of Goods is expressly excluded Any law or regulation which provides that the language of a contract shall be construed against the drafter shall not apply to this License 114 UNIVERSITY AT BUFFALO PUBLIC LICENSE UBPL VERSION 1 0 12 Responsibility for claims As between Initial Developer and the Contributors each party is responsible for claims and damages arising directly or indirectly out of its utilization of rights under this License and You agree to work with Initial Developer and Contributors to distribute such responsibility on an equitable basis Nothing herein is intended or shall be deemed to constitute any admission of liability 13 Multiple licensed code Initial Developer may designate portions of the Covered Code as Multiple Licensed Multiple Licensed means that the Initial Developer permits you to utilize portions of the Covered Code und
160. sserted wffs that dominate the terms described by pTermSet clear infer Deletes any information placed in the active connection graph version of the network clearkb Empties the knowledge base copyright Prints copyright information define frame SNePSLOGsymbol rely rel rel LispString If SNePSLOG is in Mode 3 this declares that every SNePSLOG term of the form P x1 Xn is to be represented by a node of the form relo P reli x reln Xn If relg is nil then the node will have no arc pointing to P One function symbol may be associated with at most one frame and it must be possible to uniquely determine the form of the SNePSLOG 6 3 SNEPSLOG SEMANTICS 59 term from each frame The LispString if present must contain the subststring rel for each non null rel and will be used by describe terms to construct a gloss of the term e define path SNePSRelation SNePSPath Defines a path based inference rule See 2 5 2 e demo filePath il t b bv a av nl The input is taken from the file specified by filePath until the end of file is reached The input is then reset to the previous input stream Notice that embebbed demos are allowed If filePath or omitted a menu of possible demonstrations will be printed and you will be able to choose one of them If filePath is an integer and the menu lists at least that many demonstrations the one with that number will be run Fo
161. st if necessary on the rule itself This backward inference triggered by forward inference operates whenever forward inference operates 2 If backward inference creates a subgoal that is not satisfied the subgoal remains as an active pro cess until the next time clear infer is issued If the subgoal is later matched during forward inference the backward inference that created it is resumed Forward inference that causes one of these backward inference processes to be resumed is called forward in backward chaining below Forward in backward chaining operates for whatever rules of inference normal forward chaining oper ates but sometimes it is operational even though normal forward chaining isn t Reduction inference and path based inference are used when open terms are matched during forward and backward chaining The Unique Variable Binding Rule is also enforced during matching According to this rule two different variables in one wff cannot be instantiated by different terms and no variable in a wff can be instantiated by another term in its wff Whenever a set of wffs is indicated in this section the wffs are listed in a convenient order For example in the andor n n Elimination rule any wff can be inferred not just the first one listed 6 4 SNIP INSNEPSLOG 65 Reduction Inference Ift ti tn are terms a and P are sets of terms 8 C a and t a then LOE vyen OF sep EDR PEL esr Be esar EN QecP Clp aser O
162. tes an instance of JavaSnepsAPI using the specified config_fileand interface_port and starts SNePS using information specified in the config_file The config file is normally in the JavaSnepsAPI subdirectory of the SNePS home directory and is named java_sneps_config config but ask the individual who installed SNePS for specifics 2 public JavaSnepsAPI int interface_port Creates an instance of JavaSnepsAPLI using the specified interface_port It is presumed that the user will then manually start SNePS and invoke the connection from Common Lisp using the function snepslog init Java sneps connection snepslog init Java sneps connection port classpath Initialize a connection between SNePS and Java on the specified port using the givenjava classpath string Call this function after creating the JavaSnepsAPI object in the Java process The JavaSnepsAPI class provides the methods e void tell java lang String command e java util HashSet lt java lang String gt ask java lang String command e java util HashSet lt java lang String gt askifnot java lang String command e java util HashSet lt Substitution gt askwh java lang String command 6 7 THE JAVA SNEPS API 71 e Java util HashSet lt Substitution gt askwhnot java lang String command e boolean isConnected e void endLispConnection The Substitution class provides the method e java lang String getValFromVar java lang String var See the Java Documentation for
163. than Source Code Initial Developer means the individual or entity identified as the Initial Developer in the Source Code notice required by Exhibit A Larger Work means a work which combines Covered Code or portions thereof with code not governed by the terms of this License License means this document 1 8 1 Licensable 1 9 means having the right to grant to the maximum extent possible whether at the time of the initial grant or subsequently acquired any and all of the rights conveyed herein Modifications means any addition to or deletion from the substance or structure of either the Original Code or any previous Modifications When Covered Code is released as a series of files a Modification is 107 108 UNIVERSITY AT BUFFALO PUBLIC LICENSE UBPL VERSION 1 0 a Any addition to or deletion from the contents of a file containing Original Code or previous Modifications b Any new file that contains any part of the Original Code or previous Modifications 1 10 Original Code means Source Code of computer software code which is described in the Source Code notice required by Exhibit A as Original Code and which at the time of its release under this License is not already Covered Code governed by this License 1 10 1 Patent Claims means any patent claim s now owned or hereafter acquired including without limitation method process and apparatus claims in any pate
164. the given context that dominate the nodes described by the nodesets 2symbol May be used in any find function in place of a nodeset to stand for any node The scope of these symbols is the outermost find function and all embedded find functions After return of the outermost find function symbol will be a SNePSUL variable whose value will be the set of nodes it matched deduce numb relation nodeset context specifier deducetrue numb relation nodeset context specifier deducefalse numb relation nodeset context specifier deducewh numb relation nodeset context specifier deducewhnot numb relation nodeset context specifier Like findassert but uses SNIP to back chain on any deduction rules in the specified context deducet rue returns all inferred nodes that satisfy the specification deducefalse returns all inferred nodes that sat isfy the negation of the specification deduce returns all inferred nodes that satisfy the specification and inferred nodes that satisfy the negation of the specification deducewh returns a list of substitutions for the free variables in the specification indicating the set of nodes that would be returned by deducet rue deducewhnot returns a list of substitutions for the free variables in the specification indicating the set of nodes that would be returned by deducefalse Note that only relations may appear in the specification not any other unitpaths or paths Neither may symbol
165. then domain restrict Q z P isa path from z to y path range restrict path path node If P is a path from node x to node y and Q is a path from y to node z then range restrict P Q z isa path from z to y path path If P is not one of the symbols and converse compose exception kstar kplus not or relative complement irreflexive restrict domain restrict orrange restrict then Pi Pp 1 is equivalent to compose Pi Pn 1 2 6 Operating on Contexts set context nodeset symbol Creates a context whose hypothesis set is nodeset which cannot contain pattern nodes If symbol is given that is made the name of the context otherwise defaultct becomes the name of the context set default context context name Changes the default context the value of defaultct to be context name add to context nodeset context name Adds the nodes of nodeset into the hypothesis set of the context context name If context name is omitted adds the hypotheses to defaultct remove from context nodeset context name Removes the nodes of nodeset from the hypothesis set of the context context name If context name is omitted removes the hypotheses from defaultct list context names Prints a list of all valid context names describe context context name Prints the hypothesis set all names and the value of the kinconsistent flag of the context named context name If context name is o
166. this License released under Section 6 1 and You must include a copy of this License with every copy of the Source Code You distribute You may not offer or impose any terms on any Source Code version that alters or restricts the applicable version of this License or the recipients rights hereunder However You may include an additional document offering the additional rights described in Section 3 5 3 2 Availability of Source Code Any Modification which You create or to which You contribute must be made available in Source Code form under the terms of this License either on the same media as an Executable version or via an accepted Electronic Distribution Mechanism to anyone to whom you made an Executable version available and if made available via Electronic Distribution Mechanism must remain available for at least twelve 12 months after the date it initially became available or at least six 6 months after a subsequent version of that particular Modification has been made available to such recipients You are responsible for ensuring that the Source Code version remains available even if the Electronic Distribution Mechanism is maintained by a third party 3 3 Description of Modifications You must cause all Covered Code to which You contribute to contain a file documenting the changes You made to create that Covered Code and the date of any change You must include a prominent statement that the Modification is derived directly or in
167. this checking is not done lexin file Loads a lexicon from file See Section 9 4 1 for the syntax of a lexicon file parse state trace level Enter the SNaLPS read parse print loop The optional parameters state and trace level may appear in either order If present state must be a symbol and becomes the initial state of the GATN grammar the default is S If present trace level must be an integer default 0 and becomes the trace level See below for the effects of the possible trace levels break arc state arc no break message Causes a Lisp break to occur on the arc numbered arc no in the order as input by atnin out of state state just before the test is performed if a break message is present it is passed to the Lisp format function and printed In the break package the contents of registers and SNaLPS variables may be examined and modified by using the GATN actions and forms Additionally the current configuration of the parser may be viewed by using current configuration Resuming by entering continue or continue to the break listener continues the parse at the point where it was broken This function unbreak arc and current configuration are intended to help debug grammars 79 80 CHAPTER 9 SNALPS THE SNEPS NATURAL LANGUAGE PROCESSING SYSTEM unbreak arc state arc no Removes a break from an arc previously set by break arc current configuration trace level Prints out the current configurat
168. tion of a version of the logic of relevant entailment and guarantees that every hypothesis in the origin set was relevantly used to derive p at least in that way a proposition may be derived in multiple ways and so have multiple supports Whenever a contradictory pair of propositions p and andor i 0 p is asserted the two propositions will have support sets 71 01 Tn On and 74 0N Th om Ev ery set of hypotheses o U 91 is now known to be contradictory and comprises a set of possible culprits for the contradiction At least one member of each of those sets must be chosen as a culprit and removed from the current context if the current BS is to be restored to a state of not being known to be contradictory 8 5 Choosing a Culprit 8 5 1 Automatic Culprit Choosing If the SNeRE act believe is performed on the proposition p it is assumed that belief in pis to take priority over any contradictory belief Therefore SNeBR behaves as follows 1 If andor 0 0 p is believed as an hypothesis it is chosen as the culprit If it is a derived belief assisted culprit choosing is done 2 If andor i 1 p q is believed and q is believed as an hypothesis then q is chosen as the culprit If gis a derived belief assisted culprit choosing is done 8 5 2 Assisted Culprit Choosing If automatic culprit choosing is not done the user must decide what to do about the contradictory BS with the assistance of SNeB
169. tnode context specifier Causes the actnode to be performed Deductions and assertions triggered during the performance will be made in the specified context x perform build action say objectl Hello object2 there Hello there CPU time 0 15 How the say action is defined will be explained below The other two ways to initiate action are during inference 1 If a node of the form M whenever p do a or of the form M when p do a where p is a proposition node and a is an act node is in the network and forward inference causes M to be adopted and p to be asserted then a is performed x describe adopt whenever build agent Stu state is location here do build action say objectl Hello object2 Stu M3 DO M2 ACTION SAY OBJECT1 Hello OBJECT2 Stu WHENEVER M1 AGENT STU LOCATION HERE STATE IS M3 CPU time 0 04 27 28 CHAPTER 4 SNERE THE SNEPS RATIONAL ENGINE x describe adopt when build agent Stu state is location here do build action say objectl I see object2 you re here M5 DO M3 ACTION SAY OBJECT1 Hello OBJECT2 Stu WHEN M2 AGENT STU LOCATION HERE STATE IS M5 CPU time 0 03 x add agent Stu state is location here Hello Stu I see you re here CPU time 0 06 The difference between when and whenever is that if the proposition p is d
170. turns off all initiation tracing multi print regs process Function that prints the registers of the individual process and their current values Assumes a knowledge of how SNIP is implemented Intended for implementing and debugging new features of SNIP snip send request snip send reports These two functions may profitably be traced by someone familiar with how SNIP is implemented Tracing snip send request will show the requests being sent the nodes they are sent to and the queue of pending processes Tracing snip send reports will show the reports being sent the channels they are being sent through and the reports coming out of the channels Chapter 4 SNeRE The SNePS Rational Engine 4 1 Acting SNeRE The SNePS Rational Engine is a package that allows for the smooth incorporation of acting into SNePS based agents SNeRE recognizes a node with an act ion arc to be a special kind of node called an act node Since an act usually consists of an action and one or more objects of the action an act node usually has additional arcs pointing to the nodes that represent the objects of the action These additional arcs are generally labelled ob ject1 objectn where n is the number of objects the action is performed on The relations object1 and object2 are pre defined If more objecti are needed the user must define them There are three ways to initiate acting The first is by use of the SNePSUL command perform perform ac
171. uality Whether the verb group is negated Possible values are affirmative Affirmative Default neg Negative Alternatives nega negative not negated modal A modal to be included in the verb group Possible values are will shall can must and may wordize number lexeme Returns the singular or plural form of lexeme according to number lexeme should be a string but a symbol or node will be coerced into a string and is presumed to be a noun If number is nil or sing the singular form will be returned otherwise the plural form will be returned The singular form will be the value of the root feature of lexeme if it has one otherwise it will be lexeme itself The plural will be formed regularly unless there is a lexical entry for lexeme that contains a plur feature Regular plural formation is sensitive to an extensive set of spelling rules 9 5 Examples In this section we present an example lexicon and two example GATNs both of which use the one lexicon The GATN in Section 9 5 1 produces parse trees of acceptable sentences The one in Section 9 5 2 builds SNePS representations of the information in statements and answers questions Both GATNs accept the same fragment of English 9 5 1 Producing Parse Trees Here is an example of the use of SNaLPS to parse simple sentences and return parse trees The GATN is shown below It is presented graphically in Figure 9 1 s jump sl t setf x parse treesx t T
172. ure 4 1 shows the three act nodes that were performed The attach primact ion call shown above associated action node SAY with the primitive action function sayfun action node M1 with the primitive action function exclaimfun and action node B1 with the primitive action function questionfun The user must remember to use attach primaction to associate action nodes even with the built in primitive action functions she intends to use As a reminder the built in action functions are listed in Table 4 1 The achieve primitive action function will be described below Table 4 1 Built in Primitive Action Functions believe disbelieve adopt unadopt achieve do one do all snsequence snif sniterate withsome withall 4 4 Defined Acts An act that is not a primitive act is called a defined act If SNeRE is asked to perform a defined act it will try to infer a plan to carry out the act A plan in the SNeRE formalism is represented by any act node but especially one whose action is a control action A node of the form M plan p act a where a is a defined act node and p is a plan node represents the proposition that the plan represented by p is the way to perform the defined act represented by a Having inferred some plans for carrying out a defined act SNeRE will perform do one on them 4 4 DEFINED ACTS 37 1 shite object action 7 object2 pe object2 p entity an vp 6 expression Io gt Figure 4 1 Three ways of associ
173. who state is location here M1 AGENT Bill LOCATION HERE STATE IS M2 AGENT Stu LOCATION HERE STATE IS M1 M2 CPU time 0 10 perform build action sniterate objectl build condition build agent Bill state is location here then build action snsequence objectl build action say objectl Hello object2 Bil1 object2 build action disbelieve objectl build agent Bill state is location here build condition build agent Stu state is location here then build action snsequence objectl build action say objectl Hello object2 Stu object2 build action disbelieve objectl build agent Stu state is location here build else build action say object1l That s object2 all Hello Stu Hello Bill That s all CPU time 0 83 withall vars suchthat do else where varsis a set of variable nodes suchthat is a proposition with vars free do is an act node with vars free and else is an act node with no free variables withal1 finds all substitutions for vars for which sucht hat is asserted and performs all those instances of do If there are no such substitutions and else is present it is done 4 2 PRIMITIVE ACTS 33 describe deduce agent Swho state is location here CPU time 0 08 perform build action withall vars Sx suchthat build agent x state is location here do bui
174. whose depth in terms of arc paths exceeds depthCutoffBackx it is not pursued Also if a result is generated during forward chaining whose depth in terms of arc paths exceeds depthCutoffForwards it is not pur sued xdepthCutoffBackx and depthCutoffForward are each set by default to 10 and can be changed independently via set f 3 2 Tracing Inference The variable and functions described in this section let you turn on and off various ways of tracing SNIP s activities Following these traces requires various degrees of knowledge of how SNIP is implemented Im plementation details however are beyond the scope of this manual infertracex This variable controls an inference trace that is readily understandable by the SNePSUL user When this inference tracing is enabled a message is printed whenever a deduce is done a sub goal is generated during backward inference a sub goal matches a stored assertion a rule fires The message indicates which of these is happening and prints one or more proposition nodes or instantiated pattern nodes The possible values of xinfertracex are nil This inference tracing is disabled t Default Nodes are printed using describe surface Nodes are printed using surface See Section 2 10 ev trace process name A SNePSUL top level command for tracing MULTI processes If called with one or more arguments un quoted it turns on event tracing of those named processes If called with no
175. xp relation nodeset 1 Evaluates virtualexp which must return a virtual relation set of flat cable sets appends the list relation nodeset to each flat cable set asserts each resulting flat cable set as a SNePS molecular node and returns the set of asserted nodes dbcount nodesetexp Evaluates the SNePSUL nodeset expression nodesetexp and returns a node whose identifier looks like the number which is the number of nodes in the resulting set dbjoin relation nodesetexpl relations nodesetexp2 relations2 A virtual relation a set of flat cable sets is created and returned The virtual relation is formed by taking the nodes returned by the SNePSUL node set expression nodesetexpl and the nodes returned by the SNePSUL node set expression nodesetexp2 joining these two relations on the attribute relation and then projecting the result down the relations attributes from the first nodeset and the relations2 attributes from the second nodeset Note that relations1 and relations2 is each a list of relations dbmax nodesetexp Evaluates the SNePSUL nodeset expression nodesetexp which must evaluate to a set of nodes all of whose identifiers look like numbers and returns the node whose identifier looks like the biggest of the numbers dbmin nodesetexp Evaluates the SNePSUL nodeset expression nodesetexp which must evaluate to a set of nodes all of whose identifiers look like numbers and returns the node whose identifier loo
176. y or liability terms You offer 3 6 Distribution of Executable Versions You may distribute Covered Code in Executable form only if the requirements of Sections 3 1 3 2 3 3 3 4 and 3 5 have been met for that Covered Code and if You include a notice stating that the Source Code version of the Covered Code is available under the terms of this License including a description of how and where You have fulfilled the obligations of Section 3 2 The notice must be conspicuously included in any notice in an Executable version related documentation or collateral in which You describe recipients rights relating to the Covered Code You may distribute the Executable version of Covered Code or ownership rights under a license of Your choice which may contain terms different from this License provided that You are in 4 INABILITY TO COMPLY DUE TO STATUTE OR REGULATION 111 compliance with the terms of this License and that the license for the Executable version does not attempt to limit or alter the recipient s rights in the Source Code version from the rights set forth in this License If You distribute the Executable version under a different license You must make it absolutely clear that any terms which differ from this License are offered by You alone not by the Initial Developer or any Contributor You hereby agree to indemnify the Initial Developer and every Contributor for any liability incurred by the Initial Developer or such Contributor as

SNePS 2.7.1 User`s Manual - University at Buffalo, Computer

Contents

Download Pdf Manuals

Related Search

Related Contents