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GECCO
2007
Springer

Evolving explicit opponent models in game playing

9 years 5 months ago
Evolving explicit opponent models in game playing
Opponent models are necessary in games where the game state is only partially known to the player, since the player must infer the state of the game based on the opponent’s actions. This paper presents an architecture and a process for developing neural network game players that utilize explicit opponent models in order to improve game play against unseen opponents. The model is constructed as a mixture over a set of cardinal opponents, i.e. opponents that represent maximally distinct game strategies. The model is trained to estimate the likelihood that the opponent will make the same move as each of the cardinal opponents would in a given game situation. Experiments were performed in the game of Guess It, a simple game of imperfect information that has no optimal strategy for defeating specific opponents. Opponent modeling is therefore crucial to play this game well. Both opponent modeling and game-playing neural networks were trained using NeuroEvolution of Augmenting Topologies (...
Alan J. Lockett, Charles L. Chen, Risto Miikkulain
Added 07 Jun 2010
Updated 07 Jun 2010
Type Conference
Year 2007
Where GECCO
Authors Alan J. Lockett, Charles L. Chen, Risto Miikkulainen
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