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SSeSeriereiresi esI sSI SISSSNSNN: :1 :19 19393939-9-4-46460608088 GGG MMM EEE BBB SSSYYYNNNTTTHHHEEESSSIISISS L LLEEECCCTTTUUURRREEESSS O OONNN A AARRRTTTIIFIFFIICICCIIAIAALLL SERSERSER &&&CCC MMMooorrrgggaaannn & & & CCClllaaayyypppoooooolll PPPuuubbbllliiissshhheeerrrsss • • • IIINNNTTTEEELLLLLLIIGIGGEEENNNCCCEEE A AANNNDDD M MMAAACCCHHHIININNEEE L LLEEEAAARRRNNNIININNGGG K K K AAA MMM III NNN SSS KKK SSSeereririeieses sE E Eddidititotororsrs:s::RRRoononanaladldl dJ J . J.B .B Brraracachchmhmmaanan,n ,Y ,Y aYahahohoooo!o !R !R Reeseseseaeararcrchchh III ••• K K K WWWililliillailaimamm W W W. .C .C Coohohehenen,n ,C ,C Caararnrnenegegigieie eM M Meelellollolnon nU U Unniniviveverersrsisititytyy AAAnnnssswwweeerrr SSSeeettt SSSooolllvvviiinnnggg AAA UUU TTThhohomommaasas sG G G. .D .D Dieieitettttetererircicihch,h ,O ,O Orreregegogonon nS S tStatatatete eU U Unniniviveverersrsisititytyy FFF MMM AAA NNN AAAnnnssswwweeerrr S SSeeettt S SSooolllvvviininnggg i ininn P PPrrraaaccctttiicicceee NNN iiinnn PPPrrraaaccctttiiiccceee • • • S S S CCC MMMaarartrtitininn G G Geebebsbseserer,r ,R ,R Roololalananndd dK K Kaamammiininnsskskiki, i,B ,B Beenennjajajmammiininn K K Kaauauuffmfmmaanannnnn, ,a ,a nanndd dT T Toororsrststetenenn S S Scchchhaauauubbb HHH AAA UUU UUUnninvivieverersrsitistiyty yo o fo fP fP oPototstsddsadamamm, ,G ,G Geerermrmmaananynyy BBB AAAnnsnswswweerer rS S eSetet Pt P rProrogogrgraramammmmmininigng g( ( A(AASSPSP)P) )i si si sa a ad d edececlcalalrararatativtivieve ep p rproroboblbelelmemm s s osolovlvlivninigng ga a papppprproroaoacachch,h ,i ,ni niintitiiatiailallyllyl yt ta taialioliolrorereded dt to too mmmoododedeleilnilnigng gp p rproroboblbelelmemmss is ni ni nt ht htehe ea a rarereaea ao o fo fK fK Knnonowowwleleldedgdgege eR R Reepeprpreresesesenentnatattaitoitoinon na a nandnd dR R Reeaeasasosononinninigng g( (K (KKRRRRRR)). ).M .M Moororere er re recececenentnltylty,ly,, AAA ititsits sa a tattttrtraracactctivtivieve ec c ocomommbbibninianatatiotioinon no o fo fa fa ar ri cricihch hy y eyetet st s imsimimpplpelel em m moododedeleilnilnigng gl al alnangnguguauagagege ew w witithith hh h ihgigihgh-h-p-pepererfrofofrormrmmaanancncece es s osolovlvlivninigngg NNN SSS ccacapapapacacictiitieiteises hs h ahasas ss sp spapararkrkekeded di ni nitnetetrereresests ti tni ni nm m maananyny yo o tohthteherer ar a rarereaeasas es e vevevenen nb b ebeyeyoyonondnd dK K KRRRRRR... WWW EEE TTThhihsis ibs b obooookok kp p rpreresesesenentntsst as a ap p rpraracactcticitciacala li lni nitntrrtorododuducuctctioitoinon nt to ot oA A ASSPSP,Pa,a,iamimimininigng ga a tat u tu susinsinigng gA A ASSPSP Pl al alnangnguguauagagegeses as a nandnd ds sy sysyststeetmemmsss RRR S S S fofofror sr so solovlvlivninigng ga a papppplpilcilciacataitoitoinon np p rproroboblbelelmemmss. s.S .S tSatatrartritnitnigng gf fr froromomm t ht htehe ee e sessssesenentnitaitaila lf lof ofrormrmmaala lf lof ofuounundndadataitoitoinonsns, s,i ,ti tii tni nitnrtrotrododuducuceceses As A ASSPSP’Ps’s’s ETETET MMMaaarrrtttiininn G GGeeebbbssseeerrr S S S ssosolovlvlivninigng gt et etcechchnhnonoloololgogygy, y,m ,m moododedeleilnilnigng gl al alnangnguguauagagege ea a nandnd dm m meetehthtohodododoloololgogygy, y,w ,w whhihlieliel ei li lliullulsustsrtratrataitnitnigng gt ht htehe eo o vovevereraralall lls lso solovlvlivninigng gp p rproorcocecesesssss OOO LLL bbyby yp p rpraracactcitcitciacala le le xexaxamammpplpelelses. s. . VVV RRRooollalaannnddd K KKaaammmiininnssskkkiii III NNN GGG I I I BBBeeennnjjajaammmiininn K KKaaauuufffmmmaaannnnnn NNN P P P RRR TTTooorrrsssttteeennn S SSccchhhaaauuubbb AAA CCC TTT III CCC EEE AAAbbbooouuuttt S S YSYYNNNTTTHHHEESESISIsIss TTThhihsis ivs v ovoloululmummee ei si s ias a ap p rprinrinitnetetded dv v everesrsriosioinon no o fo fa fa aw w oworokrkr kt ht htahata ta ta pappppepeaearasrsr is ni ni nt ht htehe eS S ySynyntnhthteheseississis MMM DDDigigiigtiatiatla lL lL iLbibirbrarararyry oy of o fE fE nEngngigninieneeeererinrinigng ag an andnd dC C oComommppuputueterter Sr S cSiceiceniencnecec. e.S .S ySynyntnhthtehesesissis iLs L eLecectcututruerersess OOO RRR pprpororvovidvidiede ce co cononcncisciesies, e,o ,or oirgirgiigninianal alp lp rpererseesesnentnatattaitoitoinonsn so sof o fi fmi mimppoporotratratnantn tr ter erseesesaearacrchrch ha an andnd dd d edeveveveloelolpopmpmmeenentntt GGG AAA tototpopipcicsic,s ,sp ,p upubublbilsilhsihseheded dq q uquiucickicklkyl,yl ,yi n,i ni nd d idgigiigtiatiatl ala lan andnd dp p rpirnirnitn tf tof ofromrmrmaatast.st .sF .F oForo rm rm mooroerer ie ni nifnofofromrmrmaataitoitoinonn NNN vvisvisiitis tiw tw wwwwww.wm.m.moororgrgaganancnclcalalyaypypopoooolo.lc.lc.ocomomm &&& CCC SSSYYYNNNTTTHHHEEESSSIISISS L LLEEECCCTTTUUURRREEESSS O OONNN A AARRRTTTIIFIFFIICICCIIAIAALLL IISISBSBNBN:N: : 9 9797878-8-1-1-1-6-6060808484545-5-9-9797171-1-1-11 LLL MMMooorrrgggaaannn & & & C CClllaaayyypppoooooolll P PPuuubbbllliisisshhheeerrrsss 990900000000000 AAA YYY IIINNNTTTEEELLLLLLIIGIGGEEENNNCCCEEE A AANNNDDD M MMAAACCCHHHIININNEEE L LLEEEAAARRRNNNIININNGGG PPP OOO wwwwwwwww..m.mmooorrgrggaananncclclalayaypyppooooooll.l.c.cocoommm 999778781816160608088445459597971711111 OOO LLL RRRoononanaladldl dJ J . J.B .B Brraracachchmhmmaanan,n ,W ,W Wililliillailaimamm W W W. .C .C Coohohehenen,n ,a ,a nandnd dT T Thhohomommaasas sG G G. .D .D Dieieitettttetererircicihch,h ,S ,S eSereririeieses Es E Eddidititotororsrss Answer Set Solving in Practice Synthesis Lectures on Artificial Intelligence and Machine Learning Editors RonaldJ.Brachman,Yahoo!Labs WilliamW.Cohen,CarnegieMellonUniversity PeterStone,UniversityofTexasatAustin AnswerSetSolvinginPractice MartinGebser,RolandKaminski,BenjaminKaufmann,andTorstenSchaub 2012 PlanningwithMarkovDecisionProcesses:AnAIPerspective MausamandAndreyKolobov 2012 ActiveLearning BurrSettles 2012 ComputationalAspectsofCooperativeGameTheory GeorgiosChalkiadakis,EdithElkind,andMichaelWooldridge 2011 RepresentationsandTechniquesfor3DObjectRecognitionandSceneInterpretation DerekHoiemandSilvioSavarese 2011 AShortIntroductiontoPreferences:BetweenArtificialIntelligenceandSocialChoice FrancescaRossi,KristenBrentVenable,andTobyWalsh 2011 HumanComputation EdithLawandLuisvonAhn 2011 iii TradingAgents MichaelP.Wellman 2011 VisualObjectRecognition KristenGraumanandBastianLeibe 2011 LearningwithSupportVectorMachines ColinCampbellandYimingYing 2011 AlgorithmsforReinforcementLearning CsabaSzepesvári 2010 DataIntegration:TheRelationalLogicApproach MichaelGenesereth 2010 MarkovLogic:AnInterfaceLayerforArtificialIntelligence PedroDomingosandDanielLowd 2009 IntroductiontoSemi-SupervisedLearning XiaojinZhuandAndrewB.Goldberg 2009 ActionProgrammingLanguages MichaelThielscher 2008 RepresentationDiscoveryusingHarmonicAnalysis SridharMahadevan 2008 EssentialsofGameTheory:AConciseMultidisciplinaryIntroduction KevinLeyton-BrownandYoavShoham 2008 AConciseIntroductiontoMultiagentSystemsandDistributedArtificialIntelligence NikosVlassis 2007 IntelligentAutonomousRobotics:ARobotSoccerCaseStudy PeterStone 2007 Copyright©2013byMorgan&Claypool Allrightsreserved.Nopartofthispublicationmaybereproduced,storedinaretrievalsystem,ortransmittedin anyformorbyanymeans—electronic,mechanical,photocopy,recording,oranyotherexceptforbriefquotationsin printedreviews,withoutthepriorpermissionofthepublisher. AnswerSetSolvinginPractice MartinGebser,RolandKaminski,BenjaminKaufmann,andTorstenSchaub www.morganclaypool.com ISBN:9781608459711 paperback ISBN:9781608459728 ebook DOI10.2200/S00457ED1V01Y201211AIM019 APublicationintheMorgan&ClaypoolPublishersseries SYNTHESISLECTURESONARTIFICIALINTELLIGENCEANDMACHINELEARNING Lecture#19 SeriesEditors:RonaldJ.Brachman,Yahoo!Labs WilliamW.Cohen,CarnegieMellonUniversity PeterStone,UniversityofTexasatAustin SeriesISSN SynthesisLecturesonArtificialIntelligenceandMachineLearning Print1939-4608 Electronic1939-4616 Answer Set Solving in Practice Martin Gebser,Roland Kaminski,Benjamin Kaufmann,andTorsten Schaub UniversityofPotsdam SYNTHESISLECTURESONARTIFICIALINTELLIGENCEANDMACHINE LEARNING#19 M &C Morgan &cLaypool publishers ABSTRACT Answer set programming (ASP) is a declarative problem solving approach, initially tailored to modeling problems in the area of knowledge representation and reasoning (KRR).More recently, its attractive combination of a rich yet simple modeling language with high-performance solving capacitieshassparkedinterestinmanyotherareasevenbeyondKRR. ThisbookpresentsapracticalintroductiontoASP,aimingatusingASPlanguagesandsystems forsolvingapplicationproblems.Startingfromtheessentialformalfoundations,itintroducesASP’s solvingtechnology,modelinglanguageandmethodology,whileillustratingtheoverallsolvingprocess bypracticalexamples. KEYWORDS answersetprogramming,declarativeproblemsolving,logicprogramming vii To Pascal, and all the Ones who enriched our lives in a lasting way.

Description:
The goal of this book is to enable people to use answer set programming (ASP) for problem solving . A solution is usually extracted from the instantiation of the variables in a successful query. As mentioned A cardinality rule with an upper bound can be expressed by the following three rules (intr
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SSeSeriereiresi esI sSI SISSSNSNN: :1 :19 19393939-9-4-46460608088 GGG MMM EEE BBB SSSYYYNNNTTTHHHEEESSSIISISS L LLEEECCCTTTUUURRREEESSS O OONNN A AARRRTTTIIFIFFIICICCIIAIAALLL SERSERSER &&&CCC MMMooorrrgggaaannn & & & CCClllaaayyypppoooooolll PPPuuubbbllliiissshhheeerrrsss • • • IIINNNTTTEEELLLLLLIIGIGGEEENNNCCCEEE A AANNNDDD M MMAAACCCHHHIININNEEE L LLEEEAAARRRNNNIININNGGG K K K AAA MMM III NNN SSS KKK SSSeereririeieses sE E Eddidititotororsrs:s::RRRoononanaladldl dJ J . J.B .B Brraracachchmhmmaanan,n ,Y ,Y aYahahohoooo!o !R !R Reeseseseaeararcrchchh III ••• K K K WWWililliillailaimamm W W W. .C .C Coohohehenen,n ,C ,C Caararnrnenegegigieie eM M Meelellollolnon nU U Unniniviveverersrsisititytyy AAAnnnssswwweeerrr SSSeeettt SSSooolllvvviiinnnggg AAA UUU TTThhohomommaasas sG G G. .D .D Dieieitettttetererircicihch,h ,O ,O Orreregegogonon nS S tStatatatete eU U Unniniviveverersrsisititytyy FFF MMM AAA NNN AAAnnnssswwweeerrr S SSeeettt S SSooolllvvviininnggg i ininn P PPrrraaaccctttiicicceee NNN iiinnn PPPrrraaaccctttiiiccceee • • • S S S CCC MMMaarartrtitininn G G Geebebsbseserer,r ,R ,R Roololalananndd dK K Kaamammiininnsskskiki, i,B ,B Beenennjajajmammiininn K K Kaauauuffmfmmaanannnnn, ,a ,a nanndd dT T Toororsrststetenenn S S Scchchhaauauubbb HHH AAA UUU UUUnninvivieverersrsitistiyty yo o fo fP fP oPototstsddsadamamm, ,G ,G Geerermrmmaananynyy BBB AAAnnsnswswweerer rS S eSetet Pt P rProrogogrgraramammmmmininigng g( ( A(AASSPSP)P) )i si si sa a ad d edececlcalalrararatativtivieve ep p rproroboblbelelmemm s s osolovlvlivninigng ga a papppprproroaoacachch,h ,i ,ni niintitiiatiailallyllyl yt ta taialioliolrorereded dt to too mmmoododedeleilnilnigng gp p rproroboblbelelmemmss is ni ni nt ht htehe ea a rarereaea ao o fo fK fK Knnonowowwleleldedgdgege eR R Reepeprpreresesesenentnatattaitoitoinon na a nandnd dR R Reeaeasasosononinninigng g( (K (KKRRRRRR)). ).M .M Moororere er re recececenentnltylty,ly,, AAA ititsits sa a tattttrtraracactctivtivieve ec c ocomommbbibninianatatiotioinon no o fo fa fa ar ri cricihch hy y eyetet st s imsimimpplpelel em m moododedeleilnilnigng gl al alnangnguguauagagege ew w witithith hh h ihgigihgh-h-p-pepererfrofofrormrmmaanancncece es s osolovlvlivninigngg NNN SSS ccacapapapacacictiitieiteises hs h ahasas ss sp spapararkrkekeded di ni nitnetetrereresests ti tni ni nm m maananyny yo o tohthteherer ar a rarereaeasas es e vevevenen nb b ebeyeyoyonondnd dK K KRRRRRR... WWW EEE TTThhihsis ibs b obooookok kp p rpreresesesenentntsst as a ap p rpraracactcticitciacala li lni nitntrrtorododuducuctctioitoinon nt to ot oA A ASSPSP,Pa,a,iamimimininigng ga a tat u tu susinsinigng gA A ASSPSP Pl al alnangnguguauagagegeses as a nandnd ds sy sysyststeetmemmsss RRR S S S fofofror sr so solovlvlivninigng ga a papppplpilcilciacataitoitoinon np p rproroboblbelelmemmss. s.S .S tSatatrartritnitnigng gf fr froromomm t ht htehe ee e sessssesenentnitaitaila lf lof ofrormrmmaala lf lof ofuounundndadataitoitoinonsns, s,i ,ti tii tni nitnrtrotrododuducuceceses As A ASSPSP’Ps’s’s ETETET MMMaaarrrtttiininn G GGeeebbbssseeerrr S S S ssosolovlvlivninigng gt et etcechchnhnonoloololgogygy, y,m ,m moododedeleilnilnigng gl al alnangnguguauagagege ea a nandnd dm m meetehthtohodododoloololgogygy, y,w ,w whhihlieliel ei li lliullulsustsrtratrataitnitnigng gt ht htehe eo o vovevereraralall lls lso solovlvlivninigng gp p rproorcocecesesssss OOO LLL bbyby yp p rpraracactcitcitciacala le le xexaxamammpplpelelses. s. . VVV RRRooollalaannnddd K KKaaammmiininnssskkkiii III NNN GGG I I I BBBeeennnjjajaammmiininn K KKaaauuufffmmmaaannnnnn NNN P P P RRR TTTooorrrsssttteeennn S SSccchhhaaauuubbb AAA CCC TTT III CCC EEE AAAbbbooouuuttt S S YSYYNNNTTTHHHEESESISIsIss TTThhihsis ivs v ovoloululmummee ei si s ias a ap p rprinrinitnetetded dv v everesrsriosioinon no o fo fa fa aw w oworokrkr kt ht htahata ta ta pappppepeaearasrsr is ni ni nt ht htehe eS S ySynyntnhthteheseississis MMM DDDigigiigtiatiatla lL lL iLbibirbrarararyry oy of o fE fE nEngngigninieneeeererinrinigng ag an andnd dC C oComommppuputueterter Sr S cSiceiceniencnecec. e.S .S ySynyntnhthtehesesissis iLs L eLecectcututruerersess OOO RRR pprpororvovidvidiede ce co cononcncisciesies, e,o ,or oirgirgiigninianal alp lp rpererseesesnentnatattaitoitoinonsn so sof o fi fmi mimppoporotratratnantn tr ter erseesesaearacrchrch ha an andnd dd d edeveveveloelolpopmpmmeenentntt GGG AAA tototpopipcicsic,s ,sp ,p upubublbilsilhsihseheded dq q uquiucickicklkyl,yl ,yi n,i ni nd d idgigiigtiatiatl ala lan andnd dp p rpirnirnitn tf tof ofromrmrmaatast.st .sF .F oForo rm rm mooroerer ie ni nifnofofromrmrmaataitoitoinonn NNN vvisvisiitis tiw tw wwwwww.wm.m.moororgrgaganancnclcalalyaypypopoooolo.lc.lc.ocomomm &&& CCC SSSYYYNNNTTTHHHEEESSSIISISS L LLEEECCCTTTUUURRREEESSS O OONNN A AARRRTTTIIFIFFIICICCIIAIAALLL IISISBSBNBN:N: : 9 9797878-8-1-1-1-6-6060808484545-5-9-9797171-1-1-11 LLL MMMooorrrgggaaannn & & & C CClllaaayyypppoooooolll P PPuuubbbllliisisshhheeerrrsss 990900000000000 AAA YYY IIINNNTTTEEELLLLLLIIGIGGEEENNNCCCEEE A AANNNDDD M MMAAACCCHHHIININNEEE L LLEEEAAARRRNNNIININNGGG PPP OOO wwwwwwwww..m.mmooorrgrggaananncclclalayaypyppooooooll.l.c.cocoommm 999778781816160608088445459597971711111 OOO LLL RRRoononanaladldl dJ J . J.B .B Brraracachchmhmmaanan,n ,W ,W Wililliillailaimamm W W W. .C .C Coohohehenen,n ,a ,a nandnd dT T Thhohomommaasas sG G G. .D .D Dieieitettttetererircicihch,h ,S ,S eSereririeieses Es E Eddidititotororsrss Answer Set Solving in Practice Synthesis Lectures on Artificial Intelligence and Machine Learning Editors RonaldJ.Brachman,Yahoo!Labs WilliamW.Cohen,CarnegieMellonUniversity PeterStone,UniversityofTexasatAustin AnswerSetSolvinginPractice MartinGebser,RolandKaminski,BenjaminKaufmann,andTorstenSchaub 2012 PlanningwithMarkovDecisionProcesses:AnAIPerspective MausamandAndreyKolobov 2012 ActiveLearning BurrSettles 2012 ComputationalAspectsofCooperativeGameTheory GeorgiosChalkiadakis,EdithElkind,andMichaelWooldridge 2011 RepresentationsandTechniquesfor3DObjectRecognitionandSceneInterpretation DerekHoiemandSilvioSavarese 2011 AShortIntroductiontoPreferences:BetweenArtificialIntelligenceandSocialChoice FrancescaRossi,KristenBrentVenable,andTobyWalsh 2011 HumanComputation EdithLawandLuisvonAhn 2011 iii TradingAgents MichaelP.Wellman 2011 VisualObjectRecognition KristenGraumanandBastianLeibe 2011 LearningwithSupportVectorMachines ColinCampbellandYimingYing 2011 AlgorithmsforReinforcementLearning CsabaSzepesvári 2010 DataIntegration:TheRelationalLogicApproach MichaelGenesereth 2010 MarkovLogic:AnInterfaceLayerforArtificialIntelligence PedroDomingosandDanielLowd 2009 IntroductiontoSemi-SupervisedLearning XiaojinZhuandAndrewB.Goldberg 2009 ActionProgrammingLanguages MichaelThielscher 2008 RepresentationDiscoveryusingHarmonicAnalysis SridharMahadevan 2008 EssentialsofGameTheory:AConciseMultidisciplinaryIntroduction KevinLeyton-BrownandYoavShoham 2008 AConciseIntroductiontoMultiagentSystemsandDistributedArtificialIntelligence NikosVlassis 2007 IntelligentAutonomousRobotics:ARobotSoccerCaseStudy PeterStone 2007 Copyright©2013byMorgan&Claypool Allrightsreserved.Nopartofthispublicationmaybereproduced,storedinaretrievalsystem,ortransmittedin anyformorbyanymeans—electronic,mechanical,photocopy,recording,oranyotherexceptforbriefquotationsin printedreviews,withoutthepriorpermissionofthepublisher. AnswerSetSolvinginPractice MartinGebser,RolandKaminski,BenjaminKaufmann,andTorstenSchaub www.morganclaypool.com ISBN:9781608459711 paperback ISBN:9781608459728 ebook DOI10.2200/S00457ED1V01Y201211AIM019 APublicationintheMorgan&ClaypoolPublishersseries SYNTHESISLECTURESONARTIFICIALINTELLIGENCEANDMACHINELEARNING Lecture#19 SeriesEditors:RonaldJ.Brachman,Yahoo!Labs WilliamW.Cohen,CarnegieMellonUniversity PeterStone,UniversityofTexasatAustin SeriesISSN SynthesisLecturesonArtificialIntelligenceandMachineLearning Print1939-4608 Electronic1939-4616 Answer Set Solving in Practice Martin Gebser,Roland Kaminski,Benjamin Kaufmann,andTorsten Schaub UniversityofPotsdam SYNTHESISLECTURESONARTIFICIALINTELLIGENCEANDMACHINE LEARNING#19 M &C Morgan &cLaypool publishers ABSTRACT Answer set programming (ASP) is a declarative problem solving approach, initially tailored to modeling problems in the area of knowledge representation and reasoning (KRR).More recently, its attractive combination of a rich yet simple modeling language with high-performance solving capacitieshassparkedinterestinmanyotherareasevenbeyondKRR. ThisbookpresentsapracticalintroductiontoASP,aimingatusingASPlanguagesandsystems forsolvingapplicationproblems.Startingfromtheessentialformalfoundations,itintroducesASP’s solvingtechnology,modelinglanguageandmethodology,whileillustratingtheoverallsolvingprocess bypracticalexamples. KEYWORDS answersetprogramming,declarativeproblemsolving,logicprogramming vii To Pascal, and all the Ones who enriched our lives in a lasting way.

Description:
The goal of this book is to enable people to use answer set programming (ASP) for problem solving . A solution is usually extracted from the instantiation of the variables in a successful query. As mentioned A cardinality rule with an upper bound can be expressed by the following three rules (intr
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