Machine Learning Methods for Planning
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- Synopsis
- 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.
- Copyright:
- 1993
Book Details
- Book Quality:
- Publisher Quality
- Book Size:
- 554 Pages
- ISBN-13:
- 9781483221175
- Related ISBNs:
- 9781558602489
- Publisher:
- Elsevier Science
- Date of Addition:
- 07/23/21
- Copyrighted By:
- Elsevier Science & Technology
- Adult content:
- No
- Language:
- English
- Has Image Descriptions:
- No
- Categories:
- Nonfiction, Social Studies
- Submitted By:
- Bookshare Staff
- Usage Restrictions:
- This is a copyrighted book.
- Edited by:
- Steven Minton