Reasoning with Probabilistic and Deterministic Graphical Models: Exact Algorithms PDF
Preview Reasoning with Probabilistic and Deterministic Graphical Models: Exact Algorithms
Series ISSN: 1939-4608 M D E S L A CH &C & YNTHESIS ECTURES ON RTIFICIAL T Morgan Claypool Publishers E R INTELLIGENCE AND MACHINE LEARNING Series Editors: Ronald J. Brachman, Yahoo! Research Reasoning with Probabilistic William W. Cohen, Carnegie Mellon University Peter Stone, University of Texas at Austin and Deterministic R Reasoning with Probabilistic and E A S O Deterministic Graphical Models N IN Graphical Models G Exact Algorithms W IT H Rina Dechter, University of California, Irvine P Exact Algorithms R O B Graphical models (e.g., Bayesian and constraint networks, influence diagrams, and Markov decision processes) A B have become a central paradigm for knowledge representation and reasoning in both artificial intelligence and IL IS computer science in general. These models are used to perform many reasoning tasks, such as scheduling, T IC planning and learning, diagnosis and prediction, design, hardware and software verification, and bioinformatics. A N These problems can be stated as the formal tasks of constraint satisfaction and satisfiability, combinatorial D D optimization, and probabilistic inference. It is well known that the tasks are computationally hard, but research ET Rina Dechter E during the past three decades has yielded a variety of principles and techniques that significantly advanced R M the state of the art. IN IS T In this book we provide comprehensive coverage of the primary exact algorithms for reasoning with such IC G models. The main feature exploited by the algorithms is the model’s graph. We present inference-based, R A message-passing schemes (e.g., variable-elimination) and search-based, conditioning schemes (e.g., cycle- PH cutset conditioning and AND/OR search). Each class possesses distinguished characteristics and in particular IC A L has different time vs. space behavior. We emphasize the dependence of both schemes on few graph parameters M O such as the treewidth, cycle-cutset, and (the pseudo-tree) height. We believe the principles outlined here would D E serve well in moving forward to approximation and anytime-based schemes. The target audience of this book L S is researchers and students in the artificial intelligence and machine learning area, and beyond. About SYNTHESIs This volume is a printed version of a work that appears in the Synthesis M Digital Library of Engineering and Computer Science. Synthesis Lectures O provide concise, original presentations of important research and development R G topics, published quickly, in digital and print formats. For more information A N visit www.morganclaypool.com & S L A YNTHESIS ECTURES ON RTIFICIAL C & ISBN: 978-1-62705-197-2 L I M L Morgan Claypool Publishers 90000 A NTELLIGENCE AND ACHINE EARNING Y P www.morganclaypool.com O 9 781627 051972 O Ronald J. Brachman, William W. Cohen, and Peter Stone, Series Editors L Reasoning with Probabilistic and Deterministic Graphical Models: Exact Algorithms Synthesis Lectures on Artificial Intelligence and Machine Learning Editors RonaldJ.Brachman,Yahoo!Research WilliamW.Cohen,CarnegieMellonUniversity PeterStone,UniversityofTexasatAustin ReasoningwithProbabilisticandDeterministicGraphicalModels:ExactAlgorithms RinaDechter 2013 AConciseIntroductiontoModelsandMethodsforAutomatedPlanning HectorGeffnerandBlaiBonet 2013 EssentialPrinciplesforAutonomousRobotics HenryHexmoor 2013 Case-BasedReasoning:AConciseIntroduction BeatrizLópez 2013 AnswerSetSolvinginPractice MartinGebser,RolandKaminski,BenjaminKaufmann,andTorstenSchaub 2012 PlanningwithMarkovDecisionProcesses:AnAIPerspective MausamandAndreyKolobov 2012 ActiveLearning BurrSettles 2012 iv ComputationalAspectsofCooperativeGameeory GeorgiosChalkiadakis,EdithElkind,andMichaelWooldridge 2011 RepresentationsandTechniquesfor3DObjectRecognitionandSceneInterpretation DerekHoiemandSilvioSavarese 2011 AShortIntroductiontoPreferences:BetweenArtificialIntelligenceandSocialChoice FrancescaRossi,KristenBrentVenable,andTobyWalsh 2011 HumanComputation EdithLawandLuisvonAhn 2011 TradingAgents MichaelP.Wellman 2011 VisualObjectRecognition KristenGraumanandBastianLeibe 2011 LearningwithSupportVectorMachines ColinCampbellandYimingYing 2011 AlgorithmsforReinforcementLearning CsabaSzepesvári 2010 DataIntegration:eRelationalLogicApproach MichaelGenesereth 2010 MarkovLogic:AnInterfaceLayerforArtificialIntelligence PedroDomingosandDanielLowd 2009 IntroductiontoSemi-SupervisedLearning XiaojinZhuandAndrewB.Goldberg 2009 ActionProgrammingLanguages Michaelielscher 2008 v RepresentationDiscoveryusingHarmonicAnalysis SridharMahadevan 2008 EssentialsofGameeory:AConciseMultidisciplinaryIntroduction KevinLeyton-BrownandYoavShoham 2008 AConciseIntroductiontoMultiagentSystemsandDistributedArtificialIntelligence NikosVlassis 2007 IntelligentAutonomousRobotics:ARobotSoccerCaseStudy PeterStone 2007 Copyright©2013byMorgan&Claypool Allrightsreserved.Nopartofthispublicationmaybereproduced,storedinaretrievalsystem,ortransmittedin anyformorbyanymeans—electronic,mechanical,photocopy,recording,oranyotherexceptforbriefquotations inprintedreviews,withoutthepriorpermissionofthepublisher. ReasoningwithProbabilisticandDeterministicGraphicalModels:ExactAlgorithms RinaDechter www.morganclaypool.com ISBN:9781627051972 paperback ISBN:9781627051989 ebook DOI10.2200/S00529ED1V01Y201308AIM023 APublicationintheMorgan&ClaypoolPublishersseries SYNTHESISLECTURESONARTIFICIALINTELLIGENCEANDMACHINELEARNING Lecture#23 SeriesEditors:RonaldJ.Brachman,YahooResearch WilliamW.Cohen,CarnegieMellonUniversity PeterStone,UniversityofTexasatAustin SeriesISSN SynthesisLecturesonArtificialIntelligenceandMachineLearning Print1939-4608 Electronic1939-4616 Reasoning with Probabilistic and Deterministic Graphical Models: Exact Algorithms Rina Dechter UniversityofCalifornia,Irvine SYNTHESISLECTURESONARTIFICIALINTELLIGENCEANDMACHINE LEARNING#23 M &C Morgan &cLaypool publishers ABSTRACT Graphical models (e.g., Bayesian and constraint networks, influence diagrams, and Markov de- cisionprocesses)havebecomeacentralparadigmforknowledgerepresentationandreasoningin both artificial intelligence and computer science in general. ese models are used to perform many reasoning tasks, such as scheduling, planning and learning, diagnosis and prediction, de- sign,hardwareandsoftwareverification,andbioinformatics.eseproblemscanbestatedasthe formal tasks of constraint satisfaction and satisfiability, combinatorial optimization, and proba- bilisticinference.Itiswellknownthatthetasksarecomputationallyhard,butresearchduringthe past three decades has yielded a variety of principles and techniques that significantly advanced thestateoftheart. In this book we provide comprehensive coverage of the primary exact algorithms for rea- soningwithsuchmodels.emainfeatureexploitedbythealgorithmsisthemodel’sgraph.We presentinference-based,message-passingschemes(e.g.,variable-elimination)andsearch-based, conditioningschemes(e.g.,cycle-cutsetconditioningandAND/ORsearch).Eachclasspossesses distinguished characteristics and in particular has different time vs. space behavior. We empha- sizethedependenceofbothschemesonfewgraphparameterssuchasthetreewidth,cycle-cutset, and (the pseudo-tree) height. We believe the principles outlined here would serve well in mov- ing forward to approximation and anytime-based schemes. e target audience of this book is researchersandstudentsintheartificialintelligenceandmachinelearningarea,andbeyond. KEYWORDS graphical models, Bayesian networks, constraint networks, Markov networks, induced-width, treewidth, cycle-cutset, loop-cutset, pseudo-tree, bucket- elimination, variable-elimination, AND/OR search, conditioning, reasoning, inference,knowledgerepresentation
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