- Häftad (Paperback / softback)
- Antal sidor
- Softcover reprint of the original 1st ed. 1983
- Springer-Verlag Berlin and Heidelberg GmbH & Co. K
- Carbonell, J. G. (ed.), Michalski, R. S. (ed.), Mitchell, T. M. (ed.)
- 25 Illustrations, black and white; XI, 572 p. 25 illus.
- 244 x 170 x 30 mm
- Antal komponenter
- 1 Paperback / softback
- 926 g
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Brave New Work
An Artificial Intelligence Approach1049Skickas inom 10-15 vardagar.
Fri frakt inom Sverige för privatpersoner.The ability to learn is one of the most fundamental attributes of intelligent behavior. Consequently, progress in the theory and computer modeling of learn ing processes is of great significance to fields concerned with understanding in telligence. Such fields include cognitive science, artificial intelligence, infor mation science, pattern recognition, psychology, education, epistemology, philosophy, and related disciplines. The recent observance of the silver anniversary of artificial intelligence has been heralded by a surge of interest in machine learning-both in building models of human learning and in understanding how machines might be endowed with the ability to learn. This renewed interest has spawned many new research projects and resulted in an increase in related scientific activities. In the summer of 1980, the First Machine Learning Workshop was held at Carnegie-Mellon University in Pittsburgh. In the same year, three consecutive issues of the Inter national Journal of Policy Analysis and Information Systems were specially devoted to machine learning (No. 2, 3 and 4, 1980). In the spring of 1981, a special issue of the SIGART Newsletter No. 76 reviewed current research projects in the field. . This book contains tutorial overviews and research papers representative of contemporary trends in the area of machine learning as viewed from an artificial intelligence perspective. As the first available text on this subject, it is intended to fulfill several needs.
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One General Issues in Machine Learning.- 1 An Overview of Machine Learning.- 2 Why Should Machines Learn?.- Two Learning from Examples.- 3 A Comparative Review of Selected Methods for Learning from Examples.- 4 A Theory and Methodology of Inductive Learning.- Three Learning in Problem-Solving and Planning.- 5 Learning by Analogy: Formulating and Generalizing Plans from Past Experience.- 6 Learning by Experimentation: Acquiring and Refining Problem-Solving Heuristics.- 7 Acquisition of Proof Skills in Geometry.- 8 Using Proofs and Refutations to Learn from Experience.- Four Learning from Observation and Discovery.- 9 The Role of Heuristics in Learning by Discovery: Three Case Studies.- 10 Rediscovering Chemistry With the BACON System.- 11 Learning From Observation: Conceptual Clustering.- Five Learning from Instruction.- 12 Machine Transformation of Advice into a Heuristic Search Procedure.- 13 Learning by Being Told: Acquiring Knowledge for Information Management.- 14 The Instructible Production System: A Retrospective Analysis.- Six Applied Learning Systems.- 15 Learning Efficient Classification Procedures and their Application to Chess End Games.- 16 Inferring Student Models for Intelligent Computer-Aided Instruction.- Comprehensive Bibliography of Machine Learning.- Glossary of Selected Terms In Machine Learning.- About the Authors.- Author Index.