Using Decision Tree Confidence Factors for Multiagent Control

Peter Stone and Manuela Veloso
Computer Science Department
Carnegie Mellon University
Pittsburgh, PA 15213
{pstone,veloso}@cs.cmu.edu
http://www.cs.cmu.edu/{"7E pstone,"7E mmv}
Keywords: multiagent systems, machine learning, decision trees

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. The newly created behavior is tested empirically in game situations.

• Introduction
• Foundational Work
  • Robotic Soccer
  • Layered Learning
• Using the learned behaviors
  • Receiver Choice Functions
  • Reasoning about action execution time
  • Incorporating the RCF in a behavior
• Experiments
• Discussion and Conclusion
• References
• About this document ...