Research Papers -- An Output-Polynomial Time Algorithm for Mining Frequent Closed Attribute Trees -- Guiding Inference Through Relational Reinforcement Learning -- Converting Semantic Meta-knowledge into Inductive Bias -- Learning Teleoreactive Logic Programs from Problem Solving -- A Framework for Set-Oriented Computation in Inductive Logic Programming and Its Application in Generalizing Inverse Entailment -- Distance Based Generalisation -- Automatic Induction of Abduction and Abstraction Theories from Observations -- Logical Bayesian Networks and Their Relation to Other Probabilistic Logical Models -- Strategies to Parallelize ILP Systems -- Inducing Causal Laws by Regular Inference -- Online Closure-Based Learning of Relational Theories -- Learning Closed Sets of Labeled Graphs for Chemical Applications -- ILP Meets Knowledge Engineering: A Case Study -- Spatial Clustering of Structured Objects -- Generalization Behaviour of Alkemic Decision Trees -- Predicate Selection for Structural Decision Trees -- Induction of the Indirect Effects of Actions by Monotonic Methods -- Probabilistic First-Order Theory Revision from Examples -- Inductive Equivalence of Logic Programs -- Deriving a Stationary Dynamic Bayesian Network from a Logic Program with Recursive Loops -- A Study of Applying Dimensionality Reduction to Restrict the Size of a Hypothesis Space -- Polynomial Time Inductive Inference of TTSP Graph Languages from Positive Data -- Classifying Relational Data with Neural Networks -- Efficient Sampling in Relational Feature Spaces -- Invited Papers -- Why Computers Need to Learn About Music -- Tutorial on Statistical Relational Learning -- Machine Learning for Systems Biology -- Five Problems in Five Areas for Five Years.

This book constitutes the refereed proceedings of the 15th International Conference on Inductive Logic Programming, ILP 2005, held in Bonn, Germany, in August 2005. The 24 revised full papers presented together with the abstract of 4 invited lectures were carefully reviewed and selected for inclusion in the book. The papers address all current topics in inductive logic programming, ranging from theoretical and methodological issues to advanced applications in various areas, also including more diverse forms of non-propositional learning.