Machine Learning Methods for Planning

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.

Contenu

Preface
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
Index