Artificial intelligence through search
Author(s)
Bibliographic Information
Artificial intelligence through search
Kluwer Academic Publishers, 1992
- : uk
- : ne
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Note
Bibliography: p. [321]-324
Includes index
Description and Table of Contents
Description
This is an important textbook on artificial intelligence that uses the unifying thread of search to bring together most of the major techniques used in symbolic artificial intelligence. The authors, aware of the pitfalls of being too general or too academic, have taken a practical approach in that they include program code to illustrate their ideas. Furthermore, code is offered in both POP-11 and Prolog, thereby giving a dual perspective, highlighting the merits of these languages.
Each chapter covers one technique and divides up into three sections:
a section which introduces the technique (and its usual applications) andsuggests how it can be understood as a variant/generalisation of search;
a section which developed a `low'-level (POP-11) implementation;
a section which develops a high-level (Prolog) implementation of the technique.
The authors also include useful notes on alternative treatments to the material, further reading and exercises.
As a practical book it will be welcomed by a wide audience including, those already experienced in AI, students with some background in programming who are taking an introductory course in AI, and lecturers looking for a precise, professional and practical text book to use in their AI courses.
About the authors:
Dr Christopher Thornton has a BA in Economics, an Sc in Computer Science and a DPhil in Artificial Intelligence. Formerly a lecturer in the Department of AI at the University of Edinburgh, he is now a lecturer in AI in the School of Cognitive and Computing Sciences at the University of Sussex.
Professor Benedict du Boulay has a BSc in Physics and a PhD in Artificial Intelligence. Previously a lecturer in the Department of Computing Science at the University of Aberdeen he is currently Professor of Artificial Intelligence, also in the School of Cognitive and Computing Sciences, University of Sussex.
Table of Contents
1. Search-Related Techniques in AI. 2. Simple State Space Search. 3. State-Space Search. 4. Heuristic State-Space Search. 5. Heuristc Search of Game Trees. 6. Problem Reduction (AND/OR-Tree Search). 7. Planning (Microworld Search). 8. Parsing (Search as Analysis). 9. Expert Systems (Probabilistic Search). 10. Concept Learning (Extension-Generating Search). 11. Prolog (Search as Computation). 12. References. Introduction to POP-11 Programming. Index.
by "Nielsen BookData"