Title
A Real-Time Opponent Modeling System For Rush Football
Abstract
One drawback with using plan recognition in adversarial games is that often players must commit to a plan before it is possible to infer the opponent's intentions. In such cases, it is valuable to couple plan recognition with plan repair, particularly in multi-agent domains where complete replanning is not computationally feasible. This paper presents a method for learning plan repair policies in realtime using Upper Confidence Bounds applied to Trees (UCT). We demonstrate how these policies can be coupled with plan recognition in an American football game (Rush 2008) to create an autonomous offensive team capable of responding to unexpected changes in defensive strategy. Our realtime version of UCT learns play modifications that result in a significantly higher average yardage and fewer interceptions than either the baseline game or domain-specific heuristics. Although it is possible to use the actual game simulator to measure reward offline, to execute UCT in real-time demands a different approach; here we describe two modules for reusing data from offline UCT searches to learn accurate state and reward estimators.
Publication Date
12-1-2011
Publication Title
IJCAI International Joint Conference on Artificial Intelligence
Number of Pages
2476-2481
Document Type
Article; Proceedings Paper
Personal Identifier
scopus
DOI Link
https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-412
Copyright Status
Unknown
Socpus ID
84881079825 (Scopus)
Source API URL
https://api.elsevier.com/content/abstract/scopus_id/84881079825
STARS Citation
Laviers, Kennard and Sukthankar, Gita, "A Real-Time Opponent Modeling System For Rush Football" (2011). Scopus Export 2010-2014. 2144.
https://stars.library.ucf.edu/scopus2010/2144