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Ashwin Ram - Goal-driven learning

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In cognitive science, artificial intelligence, psychology, and education, a growing body of research supports the view that the learning process is strongly influenced by the learners goals. The fundamental tenet of goal-driven learning is that learning is largely an active and strategic process in which the learner, human or machine, attempts to identify and satisfy its information needs in the context of its tasks and goals, its prior knowledge, its capabilities, and environmental opportunities for learning. This book brings together a diversity of research on goal-driven learning to establish a broad, interdisciplinary framework that describes the goal-driven learning process. It collects and solidifies existing results on this important issue in machine and human learning and presents a theoretical framework for future investigations.The book opens with an an overview of goal-driven learning research and computational and cognitive models of the goal-driven learning process. This introduction is followed by a collection of fourteen recent research articles addressing fundamental issues of the field, including psychological and functional arguments for modeling learning as a deliberative, planful process; experimental evaluation of the benefits of utility-based analysis to guide decisions about what to learn; case studies of computational models in which learning is driven by reasoning about learning goals; psychological evidence for human goal-driven learning; and the ramifications of goal-driven learning in educational contexts.The second part of the book presents six position papers reflecting ongoing research and current issues in goal-driven learning. Issues discussed include methods for pursuing psychological studies of goal-driven learning, frameworks for the design of active and multistrategy learning systems, and methods for selecting and balancing the goals that drive learning.A Bradford Book

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Page iii
Goal-Driven Learning edited by Ashwin Ram and David B Leake A Bradford - photo 1
Goal-Driven Learning
edited by Ashwin Ram and David B. Leake
A Bradford Book
The MIT Press
Cambridge, Massachusetts
London, England

title:Goal-driven Learning
author:Ram, Ashwin.
publisher:MIT Press
isbn10 | asin:0262181657
print isbn13:9780262181655
ebook isbn13:9780585038537
language:English
subjectComputational learning theory.
publication date:1995
lcc:Q325.7.G63 1995eb
ddc:006.3/1
subject:Computational learning theory.
Page iv
1995 Massachusetts Institute of Technology
All rights reserved. No part of this book may be reproduced in any form by any electronic or mechanical means (including photocopying, recording, or information storage and retrieval) without permission in writing from the publisher.
This book was set in Palatino by Asco Trade Typesetting Ltd., Hong Kong and was printed and bound in the United States of America.
Library of Congress Cataloging-in-Publication Data
Goal-driven learning / edited by Ashwin Ram and David B. Leake.
p. cm.
"A Bradford book."
Includes bibliographical references and index.
ISBN 0-262-18165-7 (hc: alk. paper).-ISBN 0-262-68083-1 (pb: alk. paper)
1. Computational learning theory. I. Ram, Ashwin. II. Leake,
David B.
Q325.7.G63 1995
006.3'1-dc20Picture 2Picture 3Picture 4Picture 5Picture 694-49361
Picture 7Picture 8Picture 9Picture 10Picture 11Picture 12 CIP
Page v
CONTENTS
Picture 13
Forward by Tom Mitchell
xi
Picture 14
Preface
xiii
Picture 15
Sources and Acknowledgments
xv
Picture 16
Contributors
xvii
1 Learning, Goals, and Learning Goals
1
Picture 17
Ashwin Ram and David B. Leake
I Current State of the Field
39
2 Planning to Learn
41
Picture 18
Lawrence Hunter
Picture 19
This chapter presents psychological and functional arguments for modeling learning as a deliberative, planful process. It describes a theory of the kinds of knowledge about resources, actions, and states in the world required for that planful learning process.
Picture 20
3 Quantitative Results Concerning the Utility of Explanation-Based Learning
55
Picture 21
Steven Minton
Picture 22
This chapter presents an experimental evaluation of the use of reasoning about costs and benefits of potential learning to guide decisions about what to learn. It demonstrates that utility criteria have important benefits for learning in the explanation-based learning component of the PRODIGY system. The learning process of the PRODIGY system as a whole is described further in chapter 11.
Picture 23
4 The Use of Explicit Goals for Knowledge to Guide Inference and Learning
83
Picture 24
Ashwin Ram and Lawrence Hunter
Picture 25
This chapter presents two computational models that serve as case studies in goal-driven learning in two very different task

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