De Raedt L., Frasconi P., Kersting K., Muggleton S. (eds.) Probabilistic Inductive Logic Programming: Theory and Applications

Berlin: Springer, 2008. — 347 p.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 simultaneously. 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 behavioral comparison of probabilistic logic programming representations and a model-theoretic expressivity analysis.Probabilistic Inductive Logic Programming
Relational Sequence Learning
Learning with Kernels and Logical Representations
Markov Logic
New Advances in Logic-Based Probabilistic Modeling by PRISM
CLP( $\cal{BN}$ ): Constraint Logic Programming for Probabilistic Knowledge
Basic Principles of Learning Bayesian Logic Programs
The Independent Choice Logic and Beyond
Protein Fold Discovery Using Stochastic Logic Programs
Probabilistic Logic Learning from Haplotype Data
Model Revision from Temporal Logic Properties in Computational Systems Biology
A Behavioral Comparison of Some Probabilistic Logic Models
Model-Theoretic Expressivity Analysis