IMPLANT: An Integrated MDP and POMDP Learning AgeNT for Adaptive Games

AAI Press
Publication Type:
Conference Proceeding
Proceedings of The Artificial Intelligence and Interactive Digital Entertainment Conference, 2009, pp. 94 - 99
Issue Date:
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This paper proposes an Integrated MDP and POMDP Learning AgeNT (IMPLANT) architecture for adaptation in modern games. The modern game world basically involves a human player acting in a virtual environment, which implies that the problem can be decomposed into two parts, namely a partially observable player model, and a completely observable game environment. With this concept, the IMPLANT ture extracts both a POMDP and MDP abstract model underlying game world. The abstract action policies are then pre-computed from each model and merged into a single optimal policy. Coupled with a small amount of online learning, the architecture is able to adapt both the player and the game environment in plausible pre-computation and query times. Empirical proof of concept is shown based on an implementation in a tennis video game, where the IMPLANT agent is shown to exhibit a superior balance in adaptation performance and speed, when compared against other agent implementation
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