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Evolving Teams of Cooperating Agents for Real-Time Strategy Game

  • Paweł Lichocki
  • Krzysztof Krawiec
  • Wojciech Jaśkowski
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5484)

Abstract

We apply gene expression programing to evolve a player for a real-time strategy (RTS) video game. The paper describes the game, evolutionary encoding of strategies and the technical implementation of experimental framework. In the experimental part, we compare two setups that differ with respect to the used approach of task decomposition. One of the setups turns out to be able to evolve an effective strategy, while the other leads to more sophisticated yet inferior solutions. We discuss both the quantitative results and the behavioral patterns observed in the evolved strategies.

Keywords

Move Vector Obstacle Avoidance Gene Expression Programming Cooperate Agent Static Obstacle 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Paweł Lichocki
    • 1
  • Krzysztof Krawiec
    • 2
  • Wojciech Jaśkowski
    • 2
  1. 1.Poznan Supercomputing and Networking CenterPoznańPoland
  2. 2.Institute of Computing SciencePoznan University of TechnologyPoznańPoland

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