資料來源: Google Book
Real-time search for learning autonomous agents
- 作者: Ishida, Toru.
- 出版: Boston : Kluwer Academic Publishers ©1997.
- 稽核項: 1 online resource (xiv, 126 pages) :illustrations.
- 叢書名: The Kluwer international series in engineering and computer science ;SECS 406
- 標題: Enterprise ApplicationsBusiness Intelligence Tools. , Electronic books. , Apprentissage automatique. , Agents intelligents (logiciels) , COMPUTERS Intelligence (AI) & Semantics. , COMPUTERS , Intelligent agents (Computer software) , Real-time data processing. , Intelligence artificielle répartie. , COMPUTERS Enterprise Applications -- Business Intelligence Tools. , Temps réel (informatique) , Intelligence (AI) & Semantics.
- ISBN: 0585345074 , 9780585345079
- ISBN: 0792399447 , 9780792399445
- 試查全文@TNUA:
- 附註: Includes bibliographical references (pages 119-123) and index.
- 摘要: 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.
- 電子資源: https://dbs.tnua.edu.tw/login?url=https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=39557
- 系統號: 005293729
- 資料類型: 電子書
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- 引用網址: 複製連結
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.
來源: Google Book
來源: Google Book
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