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Towards Collaborative and Adversarial Learning: A Case Study in Robotic Soccer.
Peter
Stone and Manuela Veloso.
International Journal of Human-Computer
Studies, 48(1):83–104, January 1998.
HTML
version.
[PDF]137.7kB [postscript]3.2MB
Soccer is a rich domain for the study of multiagent learning issues. Not only must the players learn low-level skills, but they must also learn to work together and to adapt to the behaviors of different opponents. We are using a robotic soccer system to study these different types of multiagent learning: low-level skills, collaborative, and adversarial. Here we describe in detail our experimental framework. We present a learned, robust, low-level behavior that is necessitated by the multiagent nature of the domain, namely shooting a moving ball. We then discuss the issues that arise as we extend the learning scenario to require collaborative and adversarial learning.
@Article(IJHCS,
Author="Peter Stone and Manuela Veloso",
Title= "Towards Collaborative and Adversarial Learning: A Case Study in Robotic Soccer",
Journal= "International Journal of Human-Computer
Studies",year="1998",volume="48",number="1",month="January",
pages="83--104",
abstract={
Soccer is a rich domain for the study of multiagent
learning issues. Not only must the players learn
low-level skills, but they must also learn to work
together and to adapt to the behaviors of different
opponents. We are using a robotic soccer system to
study these different types of multiagent learning:
low-level skills, collaborative, and adversarial.
Here we describe in detail our experimental
framework. We present a learned, robust, low-level
behavior that is necessitated by the multiagent
nature of the domain, namely shooting a moving ball.
We then discuss the issues that arise as we extend
the learning scenario to require collaborative and
adversarial learning.
},
wwwnote={<a href="http://www.cs.utexas.edu/~pstone/Papers/96ijhcs/article.html">HTML version</a>.},
)
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