# Peter Stone's Selected Publications

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## Using Decision Tree Confidence Factors for Multiagent Control

Peter Stone and Manuela Veloso. Using Decision Tree Confidence Factors for Multiagent Control. In Hiroaki Kitano, editors, RoboCup-97: Robot Soccer World Cup I, Lecture Notes in Artificial Intelligence, pp. 99–111, Springer Verlag, Berlin, 1998.
HTML version.
Official version from Publisher's Webpage© Springer-Verlag

### Abstract

Although Decision Trees are widely used for classification tasks, they are typically not used for agent control. This paper presents a novel technique for agent control in a complex multiagent domain based on the confidence factors provided by the C4.5 Decision Tree algorithm. Using Robotic Soccer as an example of such a domain, this paper incorporates a previously-trained Decision Tree into a full multiagent behavior that is capable of controlling agents throughout an entire game. Along with using Decision Trees for control, this behavior also makes use of the ability to reason about action-execution time to eliminate options that would not have adequate time to be executed successfully. This multiagent behavior represents a bridge between low-level and high-level learning in the Layered Learning paradigm. The newly created behavior is tested empirically in game situations.

### BibTeX Entry

@InCollection(LNAI97-dt,
Author="Peter Stone and Manuela Veloso",
Title="Using Decision Tree Confidence Factors for Multiagent Control",
booktitle= "{R}obo{C}up-97: Robot Soccer World Cup {I}",
Editor="Hiroaki Kitano",
series="Lecture Notes in Artificial Intelligence",
volume="1395",
pages="99--111",
annote="Also in {\em Proceedings of the Second International
Conference on Autonomous Agents}, 1998",
abstract={
Although Decision Trees are widely used for
classification tasks, they are typically not used
for agent control.  This paper presents a novel
technique for agent control in a complex multiagent
domain based on the confidence factors provided by
the C4.5 Decision Tree algorithm.  Using Robotic
Soccer as an example of such a domain, this paper
incorporates a previously-trained Decision Tree into
a full multiagent behavior that is capable of
controlling agents throughout an entire game.  Along
with using Decision Trees for control, this behavior
also makes use of the ability to reason about
action-execution time to eliminate options that
would not have adequate time to be executed
successfully.  This multiagent behavior represents a
bridge between low-level and high-level learning in
the Layered Learning paradigm.  The newly created
behavior is tested empirically in game situations.
},
wwwnote={<a href="http://www.cs.utexas.edu/~pstone/Papers/97springer/dt-paper/dt-paper.html">HTML version</a>.<br>
Official version from <a href="http://dx.doi.org/10.1007/3-540-64473-3_52">Publisher's Webpage</a>&copy Springer-Verlag},
)


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