Knowledge Representation, Reasoning, and the Design of Intelligent Agents: The Answer-Set Programming Approach

366 Pages·2014·1.72 MB·English

by  Gelfond M.

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KNOWLEDGE REPRESENTATION, REASONING, AND
THE DESIGN OF INTELLIGENT AGENTS
Knowledgerepresentationandreasoningisthefoundationofartificialintel-
ligence, declarative programming, and the design of knowledge-intensive
softwaresystemscapableofperformingintelligenttasks.Usinglogicaland
probabilisticformalismsbasedonanswer-setprogramming(ASP)andac-
tionlanguages,thisbookshowshowsuchsystemscanbegivenknowledge
abouttheworldandhowthisknowledgecanbeusedtosolvenontrivialcom-
putationalproblems.Theauthorsmaintainabalancebetweenmathematical
analysisandpracticaldesignofintelligentagents.Alltheconcepts,suchas
answering queries, planning, diagnostics, and probabilistic reasoning, are
illustratedbyprogrammingexamples.ThetextcanbeusedforAI-related
undergraduate and graduate classes and by researchers who would like to
learnmoreaboutASPandknowledgerepresentation.
Dr. Michael Gelfond is a Professor of Computer Science at Texas Tech
University. He received his PhD from the Institute of Mathematics of the
Academy of Sciences, St. Petersburg, Russia. He is a AAAI fellow and
servesasanAreaEditorfortheInternationalJournalofTheoryandPrac-
tice of Logic Programming and as an Executive Editor of the Journal of
LogicandComputation.In2004and2012hewastherecipientoftheaward
formostinfluentialpaperintwentyyearsbytheInternationalAssociation
ofLogicProgramming.
Yulia Kahl is a member of the Texas Action Group and the Knowledge
RepresentationLabatTexasTech.ShereceivedherMaster’sinComputer
Science,focusingontheuseofASPforplanning.Shehasalsoworkedas
aprogrammeratIBM.
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KNOWLEDGE REPRESENTATION,
REASONING, AND THE DESIGN
OF INTELLIGENT AGENTS
The Answer-Set Programming Approach
MICHAEL GELFOND
TexasTechUniversity
YULIA KAHL
UniversityofTexasatElPaso
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32AvenueoftheAmericas,NewYork,NY10013-2473,USA
CambridgeUniversityPressispartoftheUniversityofCambridge.
ItfurtherstheUniversity’smissionbydisseminatingknowledgeinthepursuitof
education,learningandresearchatthehighestinternationallevelsofexcellence.
www.cambridge.org
Informationonthistitle:www.cambridge.org/9781107029569
(cid:2)C MichaelGelfondandYuliaKahl2014
Thispublicationisincopyright.Subjecttostatutoryexception
andtotheprovisionsofrelevantcollectivelicensingagreements,
noreproductionofanypartmaytakeplacewithoutthewritten
permissionofCambridgeUniversityPress.
Firstpublished2014
PrintedintheUnitedStatesofAmerica
AcatalogrecordforthispublicationisavailablefromtheBritishLibrary.
LibraryofCongressCataloginginPublicationData
Gelfond,Michael.
Knowledgerepresentation,reasoning,andthedesignofintelligentagents:theanswer-set
programmingapproach/MichaelGelfond,TexasTechUniversity,YuliaKahl,
UniversityofTexasatElPaso.
pagescm
Includesbibliographicalreferencesandindex.
ISBN978-1-107-02956-9(hardback:alk.paper)
1.Intelligentagents(Computersoftware) I.Kahl,Yulia,1970– II.Title.
QA76.76.I58G45 2014
006.3–dc23 2013029651
ISBN978-1-107-02956-9Hardback
CambridgeUniversityPresshasnoresponsibilityforthepersistenceoraccuracyofURLsfor
externalorthird-partyInternetWebsitesreferredtointhispublicationanddoesnotguarantee
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ToLaraandPatrick,withlove
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Contents
Preface pagexiii
1 Logic-BasedApproachtoAgentDesign 1
1.1 ModelinganIntelligentAgent 1
1.2 SimpleFamilyKnowledgeBase–anExample 4
1.3 AHistoricalComment 6
1.3.1 TheAxiomaticMethod 6
1.3.2 LogicandDesignofIntelligentAgents 7
Summary 8
ReferencesandFurtherReading 9
Exercises 10
2 AnswerSetProlog(ASP) 11
2.1 Syntax 11
2.2 Semantics 16
2.2.1 InformalSemantics 16
2.2.2 FormalSemantics 21
2.3 ANoteonTranslationfromNaturalLanguage 30
2.4 PropertiesofASPPrograms 31
Summary 35
ReferencesandFurtherReading 35
Exercises 36
3 RootsofAnswerSetProlog 40
3.1 First-OrderLogic(FOL) 40
3.2 NonmonotonicLogics 43
3.2.1 Circumscription 44
3.2.2 AutoepistemicLogic 46
3.2.3 Reiter’sDefaultTheories 47
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viii Contents
3.3 ASPandNegationinLogicProgramming 49
3.3.1 Clark’sCompletion 49
3.3.2 Well-FoundedSemantics 53
Summary 57
ReferencesandFurtherReading 57
Exercises 58
4 CreatingaKnowledgeBase 61
4.1 ReasoningaboutFamily 62
4.1.1 BasicFamilyRelationships 62
4.1.2 DefiningOrphans 66
4.1.3 DefiningAncestors 68
4.2 ReasoningaboutElectricalCircuits 71
4.3 HierarchicalInformationandInheritance 75
Summary 81
ReferencesandFurtherReading 82
Exercises 83
5 RepresentingDefaults 86
5.1 AGeneralStrategyforRepresentingDefaults 86
5.1.1 UncaringJohn 86
5.1.2 CowardlyStudents 91
5.1.3 ASpecialCase 92
5.2 KnowledgeBaseswithNullValues 93
5.2.1 CourseCatalog 93
5.3 SimplePrioritiesbetweenDefaults 95
5.4 InheritanceHierarchieswithDefaults 99
5.4.1 SubmarinesRevisited 99
5.4.2 MembershipRevisited 100
5.4.3 TheSpecificityPrinciple 100
5.5 (*)IndirectExceptionstoDefaults 103
Summary 108
ReferencesandFurtherReading 108
Exercises 109
6 TheAnswer-SetProgrammingParadigm 114
6.1 ComputingHamiltonianCycles 115
6.2 SolvingPuzzles 121
6.2.1 SudokuPuzzle 121
6.2.2 MysteryPuzzle 125