Детальная информация

Название: Layered learning in multiagent systems: a winning approach to robotic soccer
Авторы: Stone Peter
Организация: IEEE Xplore (Online Service); MIT Press
Выходные сведения: Cambridge, Massachusetts London, England: MIT Press, 2000
Коллекция: Электронные книги зарубежных издательств; MIT Press eBooks Library; Общая коллекция
Тематика: Роботы; мультиагентные системы
УДК: 621.865.8
Язык: Английский
Права доступа: Доступ по паролю из сети Интернет (чтение, печать)

Разрешенные действия: Посмотреть

Аннотация

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.

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