Inductive Logic Programming

This book constitutes the refereed proceedings of the 13th International Conference on Inductive Logic Programming, ILP 2003, held in Szeged, Hungary in September/October 2003. The 23 revised full papers presented were carefully reviewed and selected from 53 submissions. Among the topics addressed are multirelational data mining, complexity issues, theory revision, clustering, mathematical discovery, relational reinforcement learning, multirelational learning, inductive inference, description logics, grammar systems, and inductive learning.

Inhalt

Invited Papers.- A Personal View of How Best to Apply ILP.- Agents that Reason and Learn.- Research Papers.- Mining Model Trees: A Multi-relational Approach.- Complexity Parameters for First-Order Classes.- A Multi-relational Decision Tree Learning Algorithm - Implementation and Experiments.- Applying Theory Revision to the Design of Distributed Databases.- Disjunctive Learning with a Soft-Clustering Method.- ILP for Mathematical Discovery.- An Exhaustive Matching Procedure for the Improvement of Learning Efficiency.- Efficient Data Structures for Inductive Logic Programming.- Graph Kernels and Gaussian Processes for Relational Reinforcement Learning.- On Condensation of a Clause.- A Comparative Evaluation of Feature Set Evolution Strategies for Multirelational Boosting.- Comparative Evaluation of Approaches to Propositionalization.- Ideal Refinement of Descriptions in -Log.- Which First-Order Logic Clauses Can Be Learned Using Genetic Algorithms?.- Improved Distances for Structured Data.- Induction of Enzyme Classes from Biological Databases.- Estimating Maximum Likelihood Parameters for Stochastic Context-Free Graph Grammars.- Induction of the Effects of Actions by Monotonic Methods.- Hybrid Abductive Inductive Learning: A Generalisation of Progol.- Query Optimization in Inductive Logic Programming by Reordering Literals.- Efficient Learning of Unlabeled Term Trees with Contractible Variables from Positive Data.- Relational IBL in Music with a New Structural Similarity Measure.- An Effective Grammar-Based Compression Algorithm for Tree Structured Data.