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|Title:||IMPLANT: An integrated mdp and pomdp learning agent for adaptive games||Authors:||Tan, C.T.
|Issue Date:||2009||Citation:||Tan, C.T.,Cheng, H.-L. (2009). IMPLANT: An integrated mdp and pomdp learning agent for adaptive games. Proceedings of the 5th Artificial Intelligence and Interactive Digital Entertainment Conference, AIIDE 2009 : 94-99. ScholarBank@NUS Repository.||Abstract:||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 architecture extracts both a POMDP and MDP abstract model from the 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 implementations.© 2009, Association for the Advancement of Artificial.||Source Title:||Proceedings of the 5th Artificial Intelligence and Interactive Digital Entertainment Conference, AIIDE 2009||URI:||http://scholarbank.nus.edu.sg/handle/10635/78180||ISBN:||9781577354314|
|Appears in Collections:||Staff Publications|
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