TitleOpponent modeling and spatial similarity to retrieve and reuse superior plays
Publication TypeConference Paper
Year of Publication2009
AuthorsLaviers, K, Sukthankar, G, Klenk, M, Aha, DW, Molineaux, M
Conference NameICCBR Workshop on Case-Based Reasoning for Computer Games
PublisherUniversity of Washington
Conference LocationSeattle, WA
Keywordscase-based reasoning, game AI, Opponent modeling

Plays are sequences of actions to be undertaken by a collection of agents, or teammates. The success of a play depends on a number of factors including, perhaps most importantly, the opponent's play. In this paper, we present an approach for online opponent modeling and illustrate how it can be used to improve oensive performance in the Rush 2008 football simulator. In football, team behaviors have an observable spatio-temporal structure, dened 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. Using the recognized defensive play, knowledge about expected outcomes, and spatial similarity between oensive plays, we retrieve an oensive play from the case base. This play is then (partially) reused to improve an in-progress oensive play. We call this process a play switch. Empirical results indicate that spatial similarity is central to play retrieval, and that substituting only a subset of the current play yields greater improvement over a full play substitution.

Refereed DesignationRefereed
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opponent modeling
case-based reasoning
game AI