Layered Learning in Multiagent Systems: A Winning Approach to Robotic Soccer

Stone, Peter:

ISBN 10: 0262194384 / ISBN 13: 9780262194389

Edité par London: The MIT Press, 2000

A propos de cet article

272 pages with dust jacket, some signs of use, apart of that in good condition, illustrated Sprache: Englisch Gewicht in Gramm: 550.

N° de réf. du vendeur 262861

Détails bibliographiques

• Titre: Layered Learning in Multiagent Systems: A Winning Approach to Robotic Soccer
• Éditeur: London: The MIT Press
• Date de parution: 2000
• Langue: anglais
• ISBN à 10 chiffres: 0262194384
• ISBN à 13 chiffres: 9780262194389
• Reliure: 8°, half-linen
• Édition: First Edition EA.
• État de l'article: Gut
• État de la jaquette: Jaquette
• Catalogues du vendeur:
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A propos de ce titre

Synopsis

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 algorithm—team-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 adversaries—a 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.

À propos de l?auteur

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.