Title Improving offensive performance through opponent modeling
Publication TypeConference Paper
Year of Publication2009
AuthorsLaviers, K, Sukthankar, G, Molineaux, M, Aha, DW
Conference NameConference on Artificial Intelligence and Interactive Digital Entertainment
PublisherAAAI Press
Conference LocationStanford, CA
Keywordsgame AI, machine learning, Opponent modeling
Abstract

Although in theory opponent modeling can be useful in any adversarial domain, in practice it is both difficult to do accurately and to use effectively to improve game play. In this paper, we present an approach for online opponent modeling and illustrate how it can be used to improve offensive performance in the Rush 2008 football game. In football, team behaviors have an observable spatio-temporal structure, defined by the relative physical positions of team members over time; we demonstrate that this structure can be exploited to recognize football plays at a very early stage of the play using a supervised learning method. Based on the teams’ play history, our system evaluates the competitive advantage of executing a play switch based on the potential of other plays to increase the yardage gained and the similarity of the candidate plays to the current play. In this paper, we investigate two types of play switches: 1) whole team and 2) subgroup. Both types of play switches improve offensive performance, but modifying the behavior of only a key subgroup of offensive players yields greater improvements in yardage gained.

Refereed DesignationRefereed
Full Text
NRL Publication Release Number: 
09-1226-1404
pub_tags: 
opponent modeling
machine learning
game AI
key_pub_tags: