| Layered Learning in Multiagent Systems: A Winning Approach to Robotic Soccer (Intelligent Robotics and Autonomous Agents) | 
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Sales Rank: 1589772
Category: Book
Author: Peter Stone
Publisher: The MIT Press
Studio: The MIT Press
Manufacturer: The MIT Press
Label: The MIT Press
Languages: English (Original Language), English (Unknown), English (Published)
Media: Hardcover
Number Of Items: 1
Pages: 284
Shipping Weight (lbs): 1.4
Dimensions (in): 9.2 x 6.2 x 0.9
ISBN: 0262194384
Dewey Decimal Number: 006.3
EAN: 9780262194389
ASIN: 0262194384
Publication Date: March 17, 2000
Availability: Usually ships in 1-2 business days
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Product Description
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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