Probabilistic Inductive Logic Programming

Inhaltsverzeichnis

• Probabilistic Inductive Logic Programming.
• Formalisms and Systems.
• Relational Sequence Learning.
• Learning with Kernels and Logical Representations.
• Markov Logic.
• New Advances in Logic-Based Probabilistic Modeling by PRISM.
• CLP( ): Constraint Logic Programming for Probabilistic Knowledge.
• Basic Principles of Learning Bayesian Logic Programs.
• The Independent Choice Logic and Beyond.
• Applications.
• Protein Fold Discovery Using Stochastic Logic Programs.
• Probabilistic Logic Learning from Haplotype Data.
• Model Revision from Temporal Logic Properties in Computational Systems Biology.
• Theory.
• A Behavioral Comparison of Some Probabilistic Logic Models.
• Model-Theoretic Expressivity Analysis.