Bayesian Artificial Intelligence

365 Pages·2004·3.71 MB·English

Bayesian
Artificial
Intelligence
Kevin B. Korb
Ann E. Nicholson
CHAPMAN & HALL/CRC
A CRC Press Company
Boca Raton London New York Washington, D.C.
© 2004 by Chapman & Hall/CRC Press LLC
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Library of Congress Cataloging-in-Publication Data
Korb, Kevin B.
Bayesian artificial intelligence / Kevin B. Korb, Ann E. Nicholson.
p. cm. — (Chapman & Hall/CRC computer science and data analysis)
Includes bibliographical references and index.
ISBN 1-58488-387-1 (alk. paper)
1. Bayesian statistical decision theory—Data processing. 2. Machine learning. 3. Neural
networks (Computer science) I. Nicholson, Ann E. II. Title. III. Series.
QA279.5.K67 2003
519.5¢42—dc21 2003055428
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© 2004 by Chapman & Hall/CRC Press LLC
To JudeaPearland ChrisWallace
© 2004 by Chapman & Hall/CRC Press LLC
Preface
BayesianArtificialIntelligence,inourunderstanding,istheincorporationofBayes-
ianinferentialmethodsinthedevelopmentofasoftwarearchitectureforanArtificial
Intelligence(AI).Webelievethatimportantingredientsofsuchanarchitecturewill
be Bayesian networks and the Bayesian learning of Bayesian networks (Bayesian
causaldiscovery)fromobservationandexperiment. Inthisbookwepresenttheel-
ementsofBayesiannetworktechnology,automatedcausaldiscovery,learningprob-
abilitiesfromdata,andexamplesandideasabouthowtoemploythesetechnologies
in developingprobabilistic expertsystems, which we call Knowledge Engineering
withBayesianNetworks.
Thisisaverypracticalproject,becausedataminingwithBayesiannetworks(ap-
plied causal discovery) and the deployment of Bayesian networks in industry and
governmentaretwoofthemostpromisingareasinappliedAItoday.Butitisalsoa
verytheoreticalproject,becausetheachievementofaBayesianAIwouldbeamajor
theoreticalachievement.
Withourtitlethereareanumberofsubjectswecouldnaturallyinclude,buthave
not. Thus, another necessary aspect of an effective Bayesian AI will be the learn-
ingofconcepts,andhierarchiesofconcepts. Bayesianmethodsforconceptforma-
tion exist (e.g., Chris Wallace’s Snob [290]), but we do not treat them here. We
couldalsohavediscussedBayesianmethodsofclassification,polynomialcurvefit-
ting,timeseriesmodeling,etc. Wehavechosentohewclosetothethemeofusing
anddiscoveringBayesiannetworksbothbecausethisisourownmainresearcharea
and because, importantas theotherBayesian learningmethodsare, we believethe
Bayesiannetworktechnologyiscentraltotheoverallproject.
OurtextdiffersfromothersavailableonBayesiannetworksinanumberofways.
We aim at a practicaland accessible introductionto the main conceptsin the tech-
nology,whilepayingattentiontofoundationalissues. Mosttextsinthisarearequire
somewhatmoremathematicalsophisticationthanours;wepresupposeonlyabasic
understandingof algebra and calculus. Also, we give roughly equal weight to the
causaldiscoveryofnetworksandtotheBayesianinferenceproceduresusinganet-
workoncefound.Mosttextseitherignorecausaldiscoveryortreatitlightly.Richard
Neapolitan’srecentbook,LearningBayesianNetworks[200],isanexception,butit
ismoretechnicallydemandingthanours. Anotherdistinguishingfeatureofourtext
isthatweadvocateacausalinterpretationofBayesiannetworks,andwediscussthe
use of Bayesian networks for causal modeling. We also illustrate variousapplica-
tionsofthetechnologyatlength,drawinguponourownappliedresearch. Wehope
thattheseillustrationswillbeofsomeinterestandindicatesomeofthepossibilities
© 2004 by Chapman & Hall/CRC Press LLC
for the technology. Our textisaimed atadvancedundergraduatesin computersci-
encewhohavesomebackgroundin artificialintelligenceandatthosewhowish to
engageinappliedorpureresearchinBayesiannetworktechnology.
ThebookWebsiteis
http://www.csse.monash.edu.au/bai
and containsa varietyof aids for study, includingexampleBayesian networksand
datasets. Instructorscanemailusforsamplesolutionstomanyoftheproblemsin
thetext.
There are many whom we wish to acknowledge. For assistance reviewing por-
tions of the text we thank: David Albrecht, Helen Armstrong, Tali Boneh, Darren
Boulton, Mark Burgman, Steven Gardner, Lucas Hope, Finn Jensen, Emily Korb,
RichardNeapolitan,KayeStacey,VickiSteinle,CharlesTwardy,ChrisWallaceand
theCRCPressreviewer.UffeKjærulff(Hugin),BrentBoerlage(Netica)andMarek
Druzdzel(GeNIe)helpeduswiththeirsoftwarepackages,whileKevinMurphyas-
sisted with the software package summary in Appendix B. Our research partners
in variousprojectsinclude: NathalieJitnah, ScottThomson,JasonCarlton, Darren
Boulton (Bayesianpoker); Ryan McGowan, Daniel Willis, Ian Brown(ambulation
monitoring); Kaye Stacey, Tali Boneh, Liz Sonenberg, Vicki Steinle, Tim Wilkin
(intelligent tutoring); Tali Boneh, Liz Sonenberg(Matilda); Lucas Hope (VE); In-
gridZukerman,RickyMcConachy(NAG);ChrisWallace,JulianNeil,LucasHope,
HelenArmstrong,CharlesTwardy,RodneyO’Donnell,HonghuaDai(causaldiscov-
ery); Russell Kennett, Chris Ryan (seabreezeprediction). Variouscolleagueshave
beeninfluentialinourintellectualdevelopmentleadingustothisendeavor;wewish
inparticulartoacknowledge: DavidAlbrecht,MikeBrady,TomDean,ColinHow-
son, Finn Jensen, Leslie Kaelbling, Uffe Kjærulff, Jak Kirman, Noretta Koertge,
RichardNeapolitan,StuartRussell, WesleySalmon,NeilThomason,IngridZuker-
man.WethankAlanDorinforcreatingourcoverimage.Ourdedicationreflectsour
indebtednesstotwoofthegreatteachers,JudeaPearlandChrisWallace. Finally,on
a personallevelAnn wouldlike to thankherparents, Paul, andRobbie, andKevin
wouldliketothankEmilyandSu.
© 2004 by Chapman & Hall/CRC Press LLC
About the Authors
KevinB.Korb,Ph.D.,earnedhisdoctorateinthephilosophyofscienceatIndiana
University (1992) working on the philosophical foundationsfor the automation of
Bayesianreasoning. SincethenhehaslecturedatMonashUniversityin Computer
Science,combininghisinterestsinphilosophyofscienceandartificialintelligencein
workonunderstandingandautomatinginductiveinference,theuseofMMLinlearn-
ingcausaltheories,artificialevolutionofcognitiveandsocialbehaviorandmodeling
Bayesianandhumanreasoningintheautomationofargumentation.
Ann E. Nicholson, D.Phil., did her undergraduatecomputer science studies at the
UniversityofMelbourneandherdoctorateintheroboticsresearchgroupatOxford
University(1992),workingondynamicBayesiannetworksfordiscretemonitoring.
ShethenspenttwoyearsatBrownUniversityasapost-doctoralresearchfellowbe-
foretakingupalecturingpositionatMonashUniversityinComputerScience. Her
generalresearchfocusisAImethodsforreasoningunderuncertainty,whilehercur-
rentresearchincludesknowledgeengineeringwithBayesiannetworks,applications
ofBayesiannetworksandusermodeling.
© 2004 by Chapman & Hall/CRC Press LLC
Contents
Part I PROBABILISTIC REASONING
Chapter 1 Bayesian Reasoning
1.1 Reasoning under uncertainty
1.2 UncertaintyinAI
1.3 Probability calculus
1.3.1 Conditional probability theorems
1.3.2 Variables
1.4 Interpretations of probability
1.5 Bayesian philosophy
1.5.1 Bayes’theorem
1.5.2 Betting and odds
1.5.3 Expected utility
1.5.4 Dutch books
1.5.5 Bayesianreasoningexamples
1.6 ThegoalofBayesianAI
1.7 AchievingBayesianAI
1.8 AreBayesiannetworksBayesian?
1.9 Summary
1.10 Bibliographicnotes
1.11 Technicalnotes
1.12 Problems
Chapter 2 Introducing Bayesian Networks
2.1 Introduction
2.2 Bayesiannetworkbasics
2.2.1 Nodesandvalues
2.2.2 Structure
2.2.3 Conditional probabilities
2.2.4 TheMarkovproperty
2.3 ReasoningwithBayesiannetworks
2.3.1 Typesofreasoning
2.3.2 Typesofevidence
2.3.3 Reasoningwithnumbers
2.4 UnderstandingBayesiannetworks
2.4.1 Representing the joint probability distribution
© 2004 by Chapman & Hall/CRC Press LLC
xii
2.4.2 Pearl’snetworkconstructionalgorithm
2.4.3 Compactness and node ordering
2.4.4 Conditionalindependence
2.4.5 d-separation
2.5 Moreexamples
2.5.1 Earthquake
2.5.2 Metastaticcancer
2.5.3 Asia
2.6 Summary
2.7 Bibliographicnotes
2.8 Problems
Chapter 3 Inference in Bayesian Networks
3.1 Introduction
3.2 Exactinferenceinchains
3.2.1 Two node network
3.2.2 Three node chain
3.3 Exactinferenceinpolytrees
3.3.1 KimandPearl’smessagepassingalgorithm
3.3.2 Messagepassingexample
3.3.3 Algorithmfeatures
3.4 Inferencewithuncertainevidence
3.4.1 Using a virtual node
3.4.2 Virtual nodes in the message passing algorithm
3.5 Exact inference in multiply-connected networks
3.5.1 Clustering methods
3.5.2 Junctiontree
3.6 Approximateinferencewithstochasticsimulation
3.6.1 Logicsampling
3.6.2 Likelihood weighting
3.6.3 MarkovChainMonteCarlo(MCMC)
3.6.4 Usingvirtualevidence
3.6.5 Assessingapproximateinferencealgorithms
3.7 Othercomputations
3.7.1 Beliefrevision
3.7.2 Probability of evidence
3.8 Causalinference
3.9 Summary
3.10 Bibliographicnotes
3.11 Problems
© 2004 by Chapman & Hall/CRC Press LLC
xiii
Chapter 4 Decision Networks
4.1 Introduction
4.2 Utilities
4.3 Decisionnetworkbasics
4.3.1 Nodetypes
4.3.2 Footballteamexample
4.3.3 Evaluatingdecisionnetworks
4.3.4 Informationlinks
4.3.5 Feverexample
4.3.6 Typesofactions
4.4 Sequentialdecisionmaking
4.4.1 Test-actioncombination
4.4.2 Realestateinvestmentexample
4.4.3 Evaluationusingadecisiontreemodel
4.4.4 Valueofinformation
4.4.5 Directevaluationofdecisionnetworks
4.5 DynamicBayesiannetworks
4.5.1 Nodes,structureandCPTs
4.5.2 Reasoning
4.5.3 InferencealgorithmsforDBNs
4.6 Dynamicdecisionnetworks
4.6.1 Mobile robot example
4.7 Summary
4.8 Bibliographicnotes
4.9 Problems
Chapter 5 Applications of Bayesian Networks
5.1 Introduction
5.2 AbriefsurveyofBNapplications
5.2.1 Typesofreasoning
5.2.2 BNstructuresformedicalproblems
5.2.3 Othermedicalapplications
5.2.4 Non-medicalapplications
5.3 Bayesianpoker
5.3.1 Five-cardstudpoker
5.3.2 Adecisionnetworkforpoker
5.3.3 Betting with randomization
5.3.4 Bluffing
5.3.5 Experimentalevaluation
5.4 Ambulationmonitoringandfalldetection
5.4.1 Thedomain
5.4.2 TheDBNmodel
5.4.3 Case-basedevaluation
5.4.4 Anextendedsensormodel
5.5 ANiceArgumentGenerator(NAG)
© 2004 by Chapman & Hall/CRC Press LLC