Title

A Monte Carlo Approach For Football Play Generation

Abstract

Learning effective policies in multi-Agent adversarial games is a significant challenge since the search space can be prohibitively large when the actions of all the agents are considered simultaneously. Recent advances in Monte Carlo search methods have produced good results in single-Agent games like Go with very large search spaces. In this paper, we propose a variation on the Monte Carlo method, UCT (Upper Confidence Bound Trees), for multi-Agent, continuousvalued, adversarial games and demonstrate its utility at generating American football plays for Rush Football 2008. In football, like in many other multi-Agent games, the actions of all of the agents are not equally crucial to gameplay success. By automatically identifying key players from historical game play, we can focus the UCT search on player groupings that have the largest impact on yardage gains in a particular formation. Copyright © 2010, Association for the Advancement of Artificial.

Publication Date

12-1-2010

Publication Title

Proceedings of the 6th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, AIIDE 2010

Number of Pages

150-155

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

Socpus ID

84883095327 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84883095327

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