Title: Learning to Select Actions in StarCraft with Genetic Algorithms
Authors: Hsu, Wei-Lun
Chen, Ying-ping
資訊工程學系
Department of Computer Science
Keywords: Real-Time Strategy Game;Genetic Algorithm
Issue Date: 1-Jan-2016
Abstract: In numerous different types of games, the real-time strategy (RTS) ones have always been the focus of gaming competitions, and in this regard, StarCraft can arguably he considered a classic real-time strategy game. Currently, most of the artificial intelligence (AI) players for real-time strategy games cannot reach or get close to the same intelligent level of their human opponents. In order to enhance the ability of AI players and hence improve the playability of games, in this study, we make an attempt to develop for StarCraft a mechanism learning to select an appropriate action to take according to the circumstance. Our empirical results show that action selection can be learned by AI players with the optimization capability of genetic algorithms and that cooperation among identical and/or different types of units is observed. The potential future work and possible research directions are discussed. The developed source code and the obtained results are released as open source.
URI: http://hdl.handle.net/11536/146725
ISSN: 2376-6816
Journal: 2016 CONFERENCE ON TECHNOLOGIES AND APPLICATIONS OF ARTIFICIAL INTELLIGENCE (TAAI)
Begin Page: 270
End Page: 277
Appears in Collections:Conferences Paper