Probabilistic Inductive Logic Programming: Theory and Applications

Luc De Raedt, Kristian Kersting (auth.), Luc De Raedt, Paolo Frasconi, Kristian Kersting, Stephen Muggleton (eds.)3540786511, 9783540786511

The question, how to combine probability and logic with learning, is getting an increased attention in several disciplines such as knowledge representation, reasoning about uncertainty, data mining, and machine learning simulateously. This results in the newly emerging subfield known under the names of statistical relational learning and probabilistic inductive logic programming.

This book provides an introduction to the field with an emphasis on the methods based on logic programming principles. It is concerned with formalisms and systems, implementations and applications, as well as with the theory of probabilistic inductive logic programming.

The 13 chapters of this state-of-the-art survey start with an introduction to probabilistic inductive logic programming; moreover the book presents a detailed overview of the most important probabilistic logic learning formalisms and systems such as relational sequence learning techniques, using kernels with logical representations, Markov logic, the PRISM system, CLP(BN), Bayesian logic programs, and the independent choice logic. The third part provides a detailed account of some show-case applications of probabilistic inductive logic programming. The final part touches upon some theoretical investigations and includes chapters on behavioural comparison of probabilistic logic programming representations and a model-theoretic expressivity analysis.

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Table of contents :
Front Matter….Pages –
Probabilistic Inductive Logic Programming….Pages 1-27
Relational Sequence Learning….Pages 28-55
Learning with Kernels and Logical Representations….Pages 56-91
Markov Logic….Pages 92-117
New Advances in Logic-Based Probabilistic Modeling by PRISM….Pages 118-155
CLP( $cal{BN}$ ): Constraint Logic Programming for Probabilistic Knowledge….Pages 156-188
Basic Principles of Learning Bayesian Logic Programs….Pages 189-221
The Independent Choice Logic and Beyond….Pages 222-243
Protein Fold Discovery Using Stochastic Logic Programs….Pages 244-262
Probabilistic Logic Learning from Haplotype Data….Pages 263-286
Model Revision from Temporal Logic Properties in Computational Systems Biology….Pages 287-304
A Behavioral Comparison of Some Probabilistic Logic Models….Pages 305-324
Model-Theoretic Expressivity Analysis….Pages 325-339
Back Matter….Pages –