Plays as effective multiagent plans enabling opponent-adaptive play selection
- Michael Bowling, University of Alberta
- Brett Browning
- Manuela Veloso
Coordinated action for a team of robots is a challenging problem, especially in dynamic, unpredictable environments. Robot soccer is an instance of a domain where well defined goals need to be achieved by multiple executors in an adversarial setting. Such domains offer challenging multiagent planning problems that need to coordinate multiagent execution in response to other agents that are not part of our team plans. In this work, we introduce the concept of a play as a multiagent plan that combines both reactive principles, which are the focus of traditional approaches for coordinating robot actions, and deliberative principles. We further introduce the concept of a playbook as a method for seamlessly combining multiple team plans. The playbook provides a set of alternative team behaviors which form the basis for our third contribution of play adaptation. We describe how these concepts were concretely implemented in the CMDragons robot soccer team. We also show empirical results indicating the importance of adaptation in adversarial or other unpredictable environments.
Citation
M. Bowling, B. Browning, M. Veloso. "Plays as effective multiagent plans enabling opponent-adaptive play selection". ICAPS, pp 376-383, January 2004.| Keywords: | machine learning |
| Category: | In Conference |
BibTeX
@incollection{Bowling+al:ICAPS04,
author = {Michael Bowling and Brett Browning and Manuela Veloso},
title = {Plays as effective multiagent plans enabling opponent-adaptive play
selection},
Pages = {376-383},
booktitle = {},
year = 2004,
}Last Updated: January 04, 2007Submitted by William Thorne
