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A Layered Approach to Learning Client Behaviors in the RoboCup Soccer Server.
Peter
Stone and Manuela Veloso.
Applied Artificial Intelligence, 12:165–188,
1998.
HTML version.
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In the past few years, Multiagent Systems (MAS) has emerged as an active subfield of Artificial Intelligence (AI). Because of the inherent complexity of MAS, there is much interest in using Machine Learning (ML) techniques to help build multiagent systems. Robotic soccer is a particularly good domain for studying MAS and Multiagent Learning. Our approach to using ML as a tool for building Soccer Server clients involves layering increasingly complex learned behaviors. In this article, we describe two levels of learned behaviors. First, the clients learn a low-level individual skill that allows them to control the ball effectively. Then, using this learned skill, they learn a higher-level skill that involves multiple players. For both skills, we describe the learning method in detail and report on our extensive empirical testing. We also verify empirically that the learned skills are applicable to game situations.
@Article(AAI97,
Author="Peter Stone and Manuela Veloso",
Title="A Layered Approach to Learning Client Behaviors in the {R}obo{C}up Soccer Server",
Journal="Applied Artificial Intelligence",
Year="1998",
Volume="12",
pages="165--188",
abstract={
In the past few years, Multiagent Systems (MAS) has
emerged as an active subfield of Artificial
Intelligence (AI). Because of the inherent complexity
of MAS, there is much interest in using Machine
Learning (ML) techniques to help build multiagent
systems. Robotic soccer is a particularly good domain
for studying MAS and Multiagent Learning. Our approach
to using ML as a tool for building Soccer Server
clients involves layering increasingly complex learned
behaviors. In this article, we describe two levels of
learned behaviors. First, the clients learn a
low-level individual skill that allows them to control
the ball effectively. Then, using this learned skill,
they learn a higher-level skill that involves multiple
players. For both skills, we describe the learning
method in detail and report on our extensive empirical
testing. We also verify empirically that the learned
skills are applicable to game situations.
},
wwwnote={<a href="http://www.cs.utexas.edu/~pstone/Papers/97aai/final-learning.html">HTML version</a>.},
)
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