Machine Intelligence 13: Machine Intelligence and Inductive Learning

by K. Furukawa (Editor), D. Michie (Editor), S. Muggleton (Editor)

Overview

Machine Intelligence 13 ushers in an exciting new phase of artificial intelligence research, one in which machine learning has emerged as a hot-bed of new theory, as a practical tool in engineering disciplines, and as a source of material for cognitive models of the human brain. Based on the Machine Intelligence Workshop of 1992, held at Strathclyde University in Scotland, the book brings together numerous papers from some of the field's leading researchers to discuss current theoretical and practical issues. Highlights include a chapter by J.A. Robinson—the founder of modern computational logic—on the field's great forefathers John von Neumann and Alan Turing, and a chapter by Stephen Muggleton that analyzes Turing's legacy in logic and machine learning. This thirteenth volume in the renowned Machine Intelligence series remains the best source of information for the latest developments in the field. All students and researchers in artificial intelligence and machine learning will want to own a copy.

Product Details

ISBN-13:	9780198538509
Publisher:	Oxford University Press
Publication date:	10/13/1994
Series:	Machine Intelligence
Pages:	488
Product dimensions:	6.38(w) x 9.56(h) x 1.30(d)

About the Author

Keio University, Tokyo

Turing Institute, Glasgow

Oxford University Computing Laboratory

Table of Contents

1. Logic, Computers, Turing, and von Neumann2. Logic and Learning: Turing's Legacy3. A Generalization of the Least Generalization4. The Justification of Logical Theories Based on Data Compression5. Utilizing Structure Information in Concept Formation6. The Discovery of Propositions in Noisy Data7. Learning Non-deterministic Finite Automata from Queries and Counterexamples8. Machine Learning and Biomolecular Modelling9. More Than Meets the Eye: Animal Learning and Knowledge Induction10. Regulation of Human Cognition and Its Growth11. Large Heterogeneous Knowledge Basis12. Learning Optimal Chess Strategies13. A Comparative Study of Classification Algorithms14. Recent Progress with BOXES15. Building Symbolic Representations of Intuitive 0.00-Time Skills from Performance Data16. Learning Perceptually Chunked Macro Operators17. Inductively Speeding Up Logic Programs