Machine intelligence and inductive learning
Author(s)
Bibliographic Information
Machine intelligence and inductive learning
（Machine intelligence / edited by N.L. Collins & Donald Michie, 13）
Clarendon Press , Oxford University Press, 1994
Available at / 39 libraries
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Library, Research Institute for Mathematical Sciences, Kyoto University数研
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Note
Includes bibliographical references and index
Description and Table of Contents
Description
The founder of modern computational logic, J A Robinson, opens this volume with a chapter on the firld's great forefathers John van Neumann and Alan Turing. Stephen Muggleton follows with an analysis of Turing's legacy in logic and machine learning, conceiving these not in generality but as specific means of imparting knowledge into computers, a theme first articulated by Turing in the late 1940s.
The present volume records the Machine Intelligence Workshop of 1992, held at Strathclyde University's Ross Priory retreat on Loch Lomond, Scotland. Here the series entered not only its second quarter-century but a new phase. As can be seen in these pages, machine learning emerged to declare itself as a seed-bed of new theory, as a practical tool in engineering disciplines, and as material for new mental models in human sciences.
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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