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Layered Learning in Multiagent Systems: A Winning Approach to Robotic Soccer,
Peter
Stone.
MIT Press, 2000.
A book based on my Ph.D.
thesis
Contents, availability, and on-line appendices
ISBN: 0262194384
(unavailable)
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.
@book(Stone:thesisbook,
Author="Peter Stone",
title="Layered Learning in Multiagent Systems: {A} Winning Approach to Robotic Soccer",
publisher="MIT Press",
year="2000",
abstract={
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.
},
wwwnote={A book based on my <a href="http://reports-archive.adm.cs.cmu.edu/anon/1998/abstracts/98-187.html">Ph.D. thesis</a><br>
<a href="http://www.cs.utexas.edu/users/pstone/book">Contents, availability, and on-line appendices</a><br>
ISBN: 0262194384
},
)
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