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Machine Learning Methods for Planning - 1st Edition - ISBN: 9781483207742, 9781483221175

Machine Learning Methods for Planning

1st Edition

Editor: Steven Minton
eBook ISBN: 9781483221175
Imprint: Morgan Kaufmann
Published Date: 2nd August 1993
Page Count: 554
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Machine Learning Methods for Planning provides information pertinent to learning methods for planning and scheduling. This book covers a wide variety of learning methods and learning architectures, including analogical, case-based, decision-tree, explanation-based, and reinforcement learning. Organized into 15 chapters, this book begins with an overview of planning and scheduling and describes some representative learning systems that have been developed for these tasks. This text then describes a learning apprentice for calendar management. Other chapters consider the problem of temporal credit assignment and describe tractable classes of problems for which optimal plans can be derived. This book discusses as well how reactive, integrated systems give rise to new requirements and opportunities for machine learning. The final chapter deals with a method for learning problem decompositions, which is based on an idealized model of efficiency for problem-reduction search. This book is a valuable resource for production managers, planners, scientists, and research workers.

Table of Contents


Chapter 1 Learning, Planning, and Scheduling: An Overview

Chapter 2 Interfaces That Learn: A Learning Apprentice for Calendar Management

Chapter 3 Reinforcement Learning for Planning and Control

Chapter 4 A First Theory of Plausible Inference and Its Use in Continuous Domain Planning

Chapter 5 Planning, Acting, and Learning in a Dynamic Domain

Chapter 6 Reactive, Integrated Systems Pose New Problems for Machine Learning

Chapter 7 Bias in Planning and Explanation-Based Learning

Chapter 8 Toward Scaling Up Machine Learning: A Case Study with Derivational Analogy in Prodigy

Chapter 9 Integration of Analogical Search Control and Explanation-Based Learning of Correctness

Chapter 10 A Unified Framework for Planning and Learning

Chapter 11 Toward a Theory of Agency

Chapter 12 Supporting Flexible Plan Reuse

Chapter 13 Adapting Plan Architectures

Chapter 14 Learning Recurring Subplans

Chapter 15 A Method for Biasing the Learning of Nonterminal Reduction Rules



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© Morgan Kaufmann 2014
2nd August 1993
Morgan Kaufmann
eBook ISBN:

About the Editor

Steven Minton

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