Machine Intelligence 13: Machine Intelligence and Inductive Learning

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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.

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Product Details

  • ISBN-13: 9780198538509
  • Publisher: Oxford University Press, USA
  • Publication date: 10/28/1994
  • Pages: 488
  • Product dimensions: 6.38 (w) x 9.56 (h) x 1.29 (d)

Meet the Author

Keio University, Tokyo

Turing Institute, Glasgow

Oxford University Computing Laboratory

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Table of Contents

1. Logic, Computers, Turing, and von Neumann
2. Logic and Learning: Turing's Legacy
3. A Generalization of the Least Generalization
4. The Justification of Logical Theories Based on Data Compression
5. Utilizing Structure Information in Concept Formation
6. The Discovery of Propositions in Noisy Data
7. Learning Non-deterministic Finite Automata from Queries and Counterexamples
8. Machine Learning and Biomolecular Modelling
9. More Than Meets the Eye: Animal Learning and Knowledge Induction
10. Regulation of Human Cognition and Its Growth
11. Large Heterogeneous Knowledge Basis
12. Learning Optimal Chess Strategies
13. A Comparative Study of Classification Algorithms
14. Recent Progress with BOXES
15. Building Symbolic Representations of Intuitive 0.00-Time Skills from Performance Data
16. Learning Perceptually Chunked Macro Operators
17. Inductively Speeding Up Logic Programs

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