Abstract
This paper describes two different decision tree-based approaches to obtain strategies that control the behavior of bots in the context of the Unreal Tournament 2004. The first approach follows the traditional process existing in commercial videogames to program the game artificial intelligence (AI), that is to say, it consists of coding the strategy manually according to the AI programmer’s experience with the aim of increasing player satisfaction. The second approach is based on evolutionary programming techniques and has the objective of automatically generating the game AI. An experimental analysis is conducted in order to evaluate the quality of our proposals. This analysis is executed on the basis of two fitness functions that were defined intuitively to provide entertainment to the player. Finally a comparison between the two approaches is done following the subjective evaluation principles imposed by the “2k bot prize” competition.
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Fernández Leiva, A.J., O’Valle Barragán, J.L. (2011). Decision Tree-Based Algorithms for Implementing Bot AI in UT2004. In: Ferrández, J.M., Álvarez Sánchez, J.R., de la Paz, F., Toledo, F.J. (eds) Foundations on Natural and Artificial Computation. IWINAC 2011. Lecture Notes in Computer Science, vol 6686. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21344-1_40
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DOI: https://doi.org/10.1007/978-3-642-21344-1_40
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