Layered Learning in Multiagent Systems: A Winning Approach to Robotic Soccer (Intelligent Robotics and Autonomous Agents Series)

Stone, Peter

ISBN 10: 0262194384 / ISBN 13: 9780262194389

Editore: MIT Press, 2000

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This is an ex-library book and may have the usual library/used-book markings inside.This book has hardback covers. In fair condition, suitable as a study copy. No dust jacket. Please note the Image in this listing is a stock photo and may not match the covers of the actual item,650grams, ISBN:0262194384.

Codice articolo 4328855

Dati bibliografici

• Titolo: Layered Learning in Multiagent Systems: A Winning Approach to Robotic Soccer (Intelligent Robotics and Autonomous Agents Series)
• Editore: MIT Press
• Data di pubblicazione: 2000
• Lingua: Inglese
• ISBN 10: 0262194384
• ISBN 13: 9780262194389
• Rilegatura: Rilegato
• Condizione: Fair
• Peso: 650 grammi
• Cataloghi dei venditori:
  • Science & Technology

Descrizione articolo

Riassunto

This book looks at multiagent systems that consist of teams of autonomous agents acting in real-time, noisy, collaborative, and adversarial environments.

This book looks at multiagent systems that consist of teams of autonomous agents acting in real-time, noisy, collaborative, and adversarial environments. The book makes four main contributions to the fields of machine learning and multiagent systems. First, it describes an architecture within which a flexible team structure allows member agents to decompose a task into flexible roles and to switch roles while acting. Second, it presents layered learning, a general-purpose machine-learning method for complex domains in which learning a mapping directly from agents' sensors to their actuators is intractable with existing machine-learning methods. Third, the book introduces a new multiagent reinforcement learning algorithmteam-partitioned, opaque-transition reinforcement learning (TPOT-RL)designed for domains in which agents cannot necessarily observe the state-changes caused by other agents' actions. The final contribution is a fully functioning multiagent system that incorporates learning in a real-time, noisy domain with teammates and adversariesa computer-simulated robotic soccer team. Peter Stone's work is the basis for the CMUnited Robotic Soccer Team, which has dominated recent RoboCup competitions. RoboCup not only helps roboticists to prove their theories in a realistic situation, but has drawn considerable public and professional attention to the field of intelligent robotics. The CMUnited team won the 1999 Stockholm simulator competition, outscoring its opponents by the rather impressive cumulative score of 110-0.

Informazioni sull?autore

Peter Stone is Assistant Professor of Computer Sciences, Alfred P. Sloan Research Fellow, and Director of the Learning Agents Group, University of Texas at Austin. He is the recipient of the International Joint Conference on Artificial Intelligence (IJCAI) 2007 Computers and Thought Award.