Bayesian Imitation Learning in Game Characters

Thurau C, Paczian T, Bauckhage C (2005)
In: International Workshop on Automatic Learning and Real-Time (ALaRT’05). 143-151.

Conference Paper | Published | English

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Abstract
As it strives to imitate observably successful actions, imitation learning allows for a quick acquisition of proven behaviors. Recent work from psychology and robotics suggests that Bayesian probability theory provides a mathematical framework for imitation learning. In this paper, we investigate the use of Bayesian imitation learning in realizing more life-like computer game characters. Following our general strategy of analyzing the network traffic of multi-player online games, we will present experiments in automatic imitation of behaviors contained in human generated data. Our results show that the Bayesian framework indeed leads to game agent behavior that appears.
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Thurau C, Paczian T, Bauckhage C. Bayesian Imitation Learning in Game Characters. In: International Workshop on Automatic Learning and Real-Time (ALaRT’05). 2005: 143-151.
Thurau, C., Paczian, T., & Bauckhage, C. (2005). Bayesian Imitation Learning in Game Characters. International Workshop on Automatic Learning and Real-Time (ALaRT’05), 143-151.
Thurau, C., Paczian, T., and Bauckhage, C. (2005). “Bayesian Imitation Learning in Game Characters” in International Workshop on Automatic Learning and Real-Time (ALaRT’05) 143-151.
Thurau, C., Paczian, T., & Bauckhage, C., 2005. Bayesian Imitation Learning in Game Characters. In International Workshop on Automatic Learning and Real-Time (ALaRT’05). pp. 143-151.
C. Thurau, T. Paczian, and C. Bauckhage, “Bayesian Imitation Learning in Game Characters”, International Workshop on Automatic Learning and Real-Time (ALaRT’05), 2005, pp.143-151.
Thurau, C., Paczian, T., Bauckhage, C.: Bayesian Imitation Learning in Game Characters. International Workshop on Automatic Learning and Real-Time (ALaRT’05). p. 143-151. (2005).
Thurau, Christian, Paczian, T., and Bauckhage, Christian. “Bayesian Imitation Learning in Game Characters”. International Workshop on Automatic Learning and Real-Time (ALaRT’05). 2005. 143-151.
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