Probabilistic Inductive Logic Programming (Google eBook)

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Luc De Raedt
Springer Science & Business Media, Mar 14, 2008 - Computers - 341 pages
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One of the key open questions within arti?cial intelligence is how to combine probability and logic with learning. This question is getting an increased - tentioninseveraldisciplinessuchasknowledgerepresentation,reasoningabout uncertainty, data mining, and machine learning simulateously, resulting in the newlyemergingsub?eldknownasstatisticalrelationallearningandprobabil- ticinductivelogicprogramming.Amajordriving forceisthe explosivegrowth in the amount of heterogeneous data that is being collected in the business and scienti?c world. Example domains include bioinformatics, chemoinform- ics, transportation systems, communication networks, social network analysis, linkanalysis,robotics,amongothers.Thestructuresencounteredcanbeass- pleassequencesandtrees(suchasthosearisinginproteinsecondarystructure predictionandnaturallanguageparsing)orascomplexascitationgraphs,the WorldWideWeb,andrelationaldatabases. This book providesan introduction to this ?eld with an emphasison those methods based on logic programming principles. The book is also the main resultofthesuccessfulEuropeanISTFETprojectno.FP6-508861onAppli- tionofProbabilisticInductiveLogicProgramming(APRILII,2004-2007).This projectwascoordinatedbytheAlbertLudwigsUniversityofFreiburg(Germany, Luc De Raedt) and the partners were Imperial College London (UK, Stephen MuggletonandMichaelSternberg),theHelsinkiInstituteofInformationTe- nology(Finland,HeikkiMannila),theUniversit` adegliStudidiFlorence(Italy, PaoloFrasconi),andtheInstitutNationaldeRechercheenInformatiqueet- tomatiqueRocquencourt(France,FrancoisFages).Itwasconcernedwiththeory, implementationsandapplicationsofprobabilisticinductivelogicprogramming. Thisstructureisalsore?ectedinthebook. The book starts with an introductory chapter to “Probabilistic Inductive LogicProgramming”byDeRaedtandKersting.Inasecondpart,itprovidesa detailedoverviewofthemostimportantprobabilisticlogiclearningformalisms and systems. We are very pleased and proud that the scientists behind the key probabilistic inductive logic programming systems (also those developed outside the APRIL project) have kindly contributed a chapter providing an overviewoftheircontributions.Thisincludes:relationalsequencelearningte- niques (Kersting et al.), using kernels with logical representations (Frasconi andPasserini),MarkovLogic(Domingosetal.), the PRISMsystem (Satoand Kameya),CLP(BN)(SantosCostaetal.),BayesianLogicPrograms(Kersting andDeRaedt),andtheIndependentChoiceLogic(Poole).Thethirdpartthen provides a detailed account of some show-caseapplications of probabilistic - ductive logic programming, more speci?cally: in protein fold discovery (Chen et al.), haplotyping (Landwehr and Mielik ̈ ainen) and systems biology (Fages andSoliman). The ?nal parttouchesupon sometheoreticalinvestigationsand VI Preface includes chaptersonbehavioralcomparisonof probabilisticlogicprogramming representations(MuggletonandChen)andamodel-theoreticexpressivityan- ysis(Jaeger).
  

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Contents

Probabilistic Inductive Logic Programming
1
Relational Sequence Learning
28
Learning with Kernels and Logical Representations
56
Markov Logic
92
New Advances in LogicBased Probabilistic Modeling by PRISM
118
Constraint Logic Programming for Probabilistic Knowledge
156
Basic Principles of Learning Bayesian Logic Programs
189
The Independent Choice Logic and Beyond
222
Protein Fold Discovery Using Stochastic Logic Programs
244
Probabilistic Logic Learning from Haplotype Data
263
Model Revision from Temporal Logic Properties in Computational Systems Biology
287
A Behavioral Comparison of Some Probabilistic Logic Models
305
ModelTheoretic Expressivity Analysis
325
Author Index
340
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About the author (2008)

Luc De Raedt is currently a full professor (C4) of computer science at the Albert-Ludwigs-University Freiburg and head of the Machine Learning lab. Before coming to Freiburg in 1999, he held positions as (parttime) senior lecturer, lecturer and assistant at the Department of Computer Science of the Katholieke Universiteit Leuven (Belgium) and as post-doc of the Fund for Scientific Research, Flanders. He obtained his undergraduate degree as well as his Ph.D. in computer science from the Katholieke Universiteit Leuven (Belgium) in 1986 and 1991. His Ph.D. thesis was subsequently published by Academic Press.

De Raedt has a rich experience in European Union research projects.He (co-)coordinated the successful ESPRIT III and IV Inductive Logic Programming (1 and 2) projects, coordinated the IST assessment project APrIL, and the Marie Curie Training Site DAISY (Foundations of Intelligent Systems). He is at present also involved in the European IST-FET project cInQ belonging to FP5.

De Raedt has (co)-organised several international workshops and conferences.