Goal-Driven Learning

Edited by Ashwin Ram and David Leake

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, 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

Table of Contents

  1. Foreword

    Tom Mitchell

  2. Preface
  3. Sources and Acknowledgments
  4. Contributors
  5. 1. Learning, Goals, and Learning Goals

    Ashwin Ram and David B. Leake

  6. I. Current State of the Field
  7. 2. Planning to Learn

    Lawrence Hunter

  8. 3. Quantitative Results Concerning the Utility of Explanation-Based Learning

    Steven Minton

  9. 4. The Use of Explicit Goals for Knowledge to Guide Inference and Learning

    Ashwin Ram and Lawrence Hunter

  10. 5. Deriving Categories to Achieve Goals

    Lawrence W. Barsalou

  11. 6. Harpoons and Long Sticks: The Interaction of Theory and Similarity in Rule Induction

    Edward J. Wisniewski and Douglas L. Medin

  12. 7. Introspective Reasoning Using Meta-Explanations for Multistrategy Learning

    Ashwin Ram and Michael T. Cox

  13. 8. Goal-Directed Learning: A Decision-Theoretic Model for Deciding What to Learn Next

    Marie desJardins

  14. 9. Goal-Based Explanation Evaluation

    David B. Leake

  15. 10. Planning to Perceive

    Louise Pryor and Gregg Collins

  16. 11. Planning and Learning in PRODIGY: Overview of an Integrated Architecture

    Jaime Carboneil, Oren Iezioni, Yolanda Gill, Robert Joseph, Craig Knoblock, Steven Minton and Manuela Veloso

  17. 12. A Learning Model for the Selection of Problem-Solving Strategies in Continuous Physical Systems

    Xiaodong Xia and Dif-Yan Yeung

  18. 13. Explicitly Biased Generalization

    Diana Gordon and Donald Perlis

  19. 14. Three Levels of Goal Orientation in Learning

    Evelyn Ng and Carl Bereiter

  20. 15. Characterizing the Application of Computer Simulations in Education: Instructional Criteria

    Jos J. A. van Berkum, Hans Hijne, Ton de Jong, Wouter R. van Joolingen and Melanie Njoo

  21. II. Current Research and Recent Directions
  22. 16. Goal-Driven Learning: Fundamental Issues and Symposium Report

    David B. Leake and Ashwin Ram

  23. 17. Storage Side Effects: Studying Processing to Understand Learning

    Lawrence W. Barsalou

  24. 18. Goal-Driven Learning in Multistrategy Reasoning and Learning Systems

    Ashwin Ram, Michael T. Cox and S. Narayanan

  25. 19. Inference to the Best Plan: A Coherence Theory of Decision

    Paul Thagard and Elija Millgram

  26. 20. Toward Goal-Driven Integration of Explanation and Action

    David B. Leake

  27. 21. Learning as Goal-Driven Inference

    Ryszard Michalski and Ashwin Ram

  28. Index