Machine Intelligence 13
Machine Intelligence and Inductive Learning
Published on 25. August 1994
Book
Hardback
488 pages
978-0-19-853850-9 (ISBN)
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.
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.
Reviews / Votes
the current volume maintains the high standards set by its predecessors... authored by distinguished researchers... * Endeavour *More details
Series
Language
English
Place of publication
Oxford
United Kingdom
Publishing group
Oxford University Press
Target group
Professional and scholarly
Illustrations
line figures, tables
Dimensions
Height: 235 mm
Width: 157 mm
Thickness: 33 mm
Weight
943 gr
ISBN-13
978-0-19-853850-9 (9780198538509)
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Schweitzer Classification
Persons
Content
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