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Evolving Strategies for Non-player Characters in Unsteady Environments

Karsten Weicker, Nicole Weicker
In: Applications of Evolutionary Computation, Hrsg: Mario Giacobini, Anthony Brabazon, Stefano Cagnoni, Gianni A. Di Caro, Aniko Ekart, Anna Isabel Esparcia-Alcazar, Muddassar Farooq, Andreas Fink, Penousal Machado, Berlin: Springer, pp. 313-322, 2009.

 

Abstract

Modern computer games place different and more diverse demands on the behavior of non-player characters in comparison to computers playing classical board games like chess. Especially the necessity for a long-term strategy conflicts often with game situations that are unsteady, i.e. many non-deterministic factors might change the possible actions. As a consequence, a computer player is needed who might take into account the danger or the chance of his actions. This work examines whether it is possible to train such a player by evolutionary algorithms. For the sake of controllable game situations, the board game Kalah is turned into an unsteady version and used to examine the problem.

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