Algorithm Selection in Zero-Sum Computer Games

Tavares, Anderson Rocha (Universidade Federal de Minas Gerais)

AAAI Conferences 

Competitive computer games are challenging domains for artificial intelligence techniques. In such games, human players often resort to strategies, or game-playing policies, to guide their low-level actions. In this research, we propose a computational version of this behavior, by modeling game playing as an algorithm selection problem: agents must map game states to algorithms to maximize their performance. By reasoning over algorithms instead of low-level actions, we reduce the complexity of decision making in computer games. With further simplifications on the state space of a game, we were able to discuss game-theoretic concepts over aspects of real-time strategy games, as well as generating a game-playing agent that successfully learns how to select algorithms in AI tournaments. We plan to further extend the approach to handle incomplete-information settings, where we do not know the possible behaviors of the opponent.