A Monte Carlo Approach for Football Play Generation


  • Kennard Laviers University of Central Florida
  • Gita Sukthankar University of Central Florida


Monte Carlo search, multi-agent adversarial games, team coordination, UCT


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, continuous-valued, 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.




How to Cite

Laviers, K., & Sukthankar, G. (2010). A Monte Carlo Approach for Football Play Generation. Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, 6(1), 150-155. Retrieved from https://ojs.aaai.org/index.php/AIIDE/article/view/12416