
Real-Time Search for Learning Autonomous Agents
Toru Ishida(Author)
Kluwer Academic Publishers
Published on 30. June 1997
Book
Hardback
XVI, 126 pages
978-0-7923-9944-5 (ISBN)
Description
Autonomous agents or multiagent systems are computational systems in which several computational agents interact or work together to perform some set of tasks. These systems may involve computational agents having common goals or distinct goals.
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.
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.
More details
Series
Edition
1997 ed.
Language
English
Place of publication
New York
United States
Target group
Professional and scholarly
Research
Product notice
sewn/stitched
Cloth over boards
Illustrations
XVI, 126 p.
Dimensions
Height: 234 mm
Width: 156 mm
Thickness: 10 mm
Weight
386 gr
ISBN-13
978-0-7923-9944-5 (9780792399445)
DOI
10.1007/b102407
Schweitzer Classification
Other editions
Additional editions

Toru Ishida
Real-Time Search for Learning Autonomous Agents
Book
03/2013
Springer
€160.49
Shipment within 15-20 days

Toru Ishida
Real-Time Search for Learning Autonomous Agents
E-Book
08/2007
Springer
€149.79
Available for download
Content
Realtime Search Performance.- Controlling Learning Processes.- Adapting to Changing Goals.- Cooperating in Uncertain Situations.- Forming Problem Solving Organizations.