Overview
Part of the book series: The Springer International Series in Engineering and Computer Science (SECS, volume 406)
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Table of contents (5 chapters)
Keywords
About this book
Real-Time Search for Learning Autonomous Agents focuses on extending real-time search algorithms for autonomous agents and for a multiagent world. Although real-time search provides an attractive framework for resource-bounded problem solving, the behavior of the problem solver is not rational enough for autonomous agents. The problem solver always keeps the record of its moves and the problem solver cannot utilize and improve previous experiments. Other problems are that although the algorithms interleave planning and execution, they cannot be directly applied to a multiagent world. The problem solver cannot adapt to the dynamically changing goals and the problem solver cannot cooperatively solve problems with other problem solvers. This book deals with all these issues.
Real-Time Search for Learning Autonomous Agents serves as an excellent resource for researchers and engineers interested in both practical references and some theoretical basis for agent/multiagent systems. The book can also be used as a text for advanced courses on the subject.
Authors and Affiliations
Bibliographic Information
Book Title: Real-Time Search for Learning Autonomous Agents
Authors: Toru Ishida
Series Title: The Springer International Series in Engineering and Computer Science
DOI: https://doi.org/10.1007/b102407
Publisher: Springer New York, NY
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eBook Packages: Springer Book Archive
Copyright Information: Springer Science+Business Media New York 1997
Hardcover ISBN: 978-0-7923-9944-5Published: 30 June 1997
Softcover ISBN: 978-1-4757-7064-3Published: 07 March 2013
eBook ISBN: 978-0-585-34507-9Published: 28 August 2007
Series ISSN: 0893-3405
Edition Number: 1
Number of Pages: XVI, 126
Topics: Special Purpose and Application-Based Systems, Artificial Intelligence, Computer Science, general