Goal-driven Learning

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Ashwin Ram, David B. Leake
MIT Press, 1995 - Computers - 507 pages
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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 learner's 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, 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.

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Contents

Learning Goals and Learning Goals
1
Planning to Learn
41
Quantitative Results Concerning the Utility
55
The Use of Explicit Goals for Knowledge to Guide Inference
83
Deriving Categories to Achieve Goals
121
The Interaction of Theory
177
Introspective Reasoning Using MetaExplanations
211
A DecisionTheoretic Model
241
Explicitly Biased Generalization
321
Three Levels of Goal Orientation in Learning
355
Characterizing the Application of Computer Simulations
381
Fundamental Issues and Symposium Report
395
Studying Processing to Understand Learning
407
GoalDriven Learning in Multistrategy Reasoning
421
A Coherence Theory of Decision
439
Toward GoalDriven Integration of Explanation and Action
455

GoalBased Explanation Evaluation
251
Planning to Perceive
287
Overview
297
A Learning Model for the Selection of ProblemSolving Strategies
307
Learning as GoalDriven Inference
479
Index
491
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About the author (1995)

Ram is Associate Professor at the College of Computing, Georgia Institute of Technology, Atlanta.

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