Latest Advances in Inductive Logic Programming

This book represents a selection of papers presented at the Inductive Logic Programming (ILP) workshop held at Cumberland Lodge, Great Windsor Park. The collection marks two decades since the first ILP workshop in 1991. During this period the area has developed into the main forum for work on logic-based machine learning. The chapters cover a wide variety of topics, ranging from theory and ILP implementations to state-of-the-art applications in real-world domains. The international contributors represent leaders in the field from prestigious institutions in Europe, North America and Asia.

Graduate students and researchers in this field will find this book highly useful as it provides an up-to-date insight into the key sub-areas of implementation and theory of ILP. For academics and researchers in the field of artificial intelligence and natural sciences, the book demonstrates how ILP is being used in areas as diverse as the learning of game strategies, robotics, natural language understanding, query search, drug design and protein modelling.

Readership: Graduate students and researchers in the field of ILP, and academics and researchers in the fields of artificial intelligence and natural sciences.

Table of Contents

Part 1: Applications
Chapter 1. Can ILP Learn Complete and Correct Game Strategies?
Chapter 2. Induction in Nonmonotonic Causal Theories for a Domestic Service Robot
Chapter 3. Using Ontologies in Semantic Data Mining with g-SEGS and Aleph
Chapter 4. Improving Search Engine Query Expansion Techniques with ILP
Chapter 5. ILP for Cosmetic Product Selection
Chapter 6. Learning User Behaviours in Real Mobile Domains
Chapter 7. Discovering Ligands for TRP Ion Channels Using Formal Concept Analysis
Chapter 8. Predictive Learning in Two-Way Datasets
Chapter 9. Model of Double-Strand Break of DNA in Logic-Based Hypothesis Finding

Part 2: Probabilistic Logical Learning
Chapter 10. The PITA System for Logical-Probabilistic Inference
Chapter 11. Learning a Generative Failure-Free PRISM Clause
Chapter 12. Statistical Relational Learning of Object Affordances for Robotic Manipulation
Chapter 13. Learning from Linked Data by Markov Logic
Chapter 14. Satisfiability Machines

Part 3: Implementations
Chapter 15. Customisable Multi-Processor Acceleration of Inductive Logic Programming
Chapter 16. Multivalue Learning in ILP
Chapter 17. Learning Dependent-Concepts in ILP: Application to Model-Driven Data Warehouses
Chapter 18. Graph Contraction Pattern Matching for Graphs of Bounded Treewidth
Chapter 19. mLynx: Relational Mutual Information

Part 4: Theory
Chapter 20. Machine Learning Coalgebraic Proofs
Chapter 21. Can ILP Deal with Incomplete and Vague Structured Knowledge?

Part 5: Logical Learning
Chapter 22. Towards Efficient Higher-Order Logic Learning in a First-Order Datalog Framework
Chapter 23. Automatic Invention of Functional Abstractions

Part 6: Constraints
Chapter 24. Using Machine-Generated Soft Constraints for Roster Problems

Part 7: Spacial and Temporal
Chapter 25. Relational Learning for Football-Related Predictions