The Instructible Production System: A Retrospective Analysis
In building systems that acquire knowledge from tutorial instruction, progress depends on determining certain functional requirements and ways for them to be met. The Instructible Production System (IPS) project has explored learning by building a series
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Machine Learning An Artificial Intelligence Approach Edited by
R. S. Michalski J. G. Carbonell T. M. Mitchell With Contributions by
J.Anderson RBanerji G.Bradshaw I.Carbonell T.Dietterich N.Haas F.Hayes-Roth G.Hendrix P.Langley D.Lenat RMichalski T.Mitchell J.Mostow B.Nudel M.Rychener RQuinlan H.Simon D. Sleeman R Stepp P. Utgoff
Springer-Verlag Berlin Heidelberg GmbH 1984
Editors: Ryszard S. Michalski University of Illinois at Urbana-Champaign, IL, USA Jaime G. Carbonell Carnegie-Mellon University Pittsburgh,PA, USA Tom M. Mitchell Rutgers University New Brunswick, NJ, USA
Springer-Verlag has the exclusive distribution right for this book outside North America
ISBN 978-3-662-12407-9 ISBN 978-3-662-12405-5 (eBook) DOI 10.1007/978-3-662-12405-5 All rights reserved. No part of this publication may be produced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior written permission of the publisher. Library of Congress Catolog Card Number 82-10654
© 1983 Springer-Verlag Berlin Heidelberg Originally published by Springer-Verlag Berlin Heidelberg New York Tokyo in 1983
2145/3140-543210
PREFACE
The ability to learn is one of the most fundamental attributes of intelligent behavior. Consequently, progress in the theory and computer modeling of learning processes is of great significance to fields concerned with understanding intelligence. Such fields include cognitive science, artificial intelligence, information 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 International 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. For researchers in artificial intelligence, computer science, and cognitive psychology, it provides an easily accessible collection of state-of-the-art papers presenting current results, which will hopefully spur further research. For students in artificial intelligence and related disciplines, this volume may serve as a supplementary textbook for a cours
