Case Indexing Using PSO and ANN in Real Time Strategy Games

  • Peng Huo
  • Simon Chi-Keung Shiu
  • HaiBo Wang
  • Ben Niu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5909)

Abstract

This paper proposes a case indexing method using particle swarm optimization (PSO) and artificial neural network (ANN) in a defense-style real time strategy (RTS) game. PSO is employed to optimize the placement of cannons to defend the enemy attack. The execution time of PSO (> 100 seconds) is unsatisfied for RTS game. The result of PSO is used as a case indexing of past experience to train ANN. After the training (approximately 30 seconds), ANN can obtain the best cannon placement within 0.05 second. Experimental results demonstrated that this case indexing method using PSO and ANN efficiently speeded up the whole process to satisfy the requirement in RTS game.

Keywords

Execution Time Particle Swarm Optimization Case Indexing Defense Base Strategy Game 
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

  • Peng Huo
    • 1
  • Simon Chi-Keung Shiu
    • 1
  • HaiBo Wang
    • 1
  • Ben Niu
    • 1
  1. 1.Department of ComputingThe Hong Kong Polytechnic UniversityHong KongChina

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