Книга Layered Learning in Multiagent Systems Peter Stone

Layered Learning in Multiagent Systems

A Winning Approach to Robotic Soccer

Автор: Peter Stone
Език: Английски език
Корици: С твърди корици
Издател: MIT Press Ltd
Наличност: 50% вероятност
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12.00 23.47 лв
This book looks at multiagent systems that consist of teams of autonomous agents acting in real-time...

Информация за книгата

Автор
Език
Английски език
Корици
Книга - С твърди корици
Издадена
2000
страници
284
EAN
9780262194389
ISBN
0262194384
Enbook ID
04561242
Издател
Теглоt
635
Размери
157 x 229 x 29

Пълно описание

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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