Evolutionary Learning in Agent-Based Combat Simulation

  • Tomonari Honda
  • Hiroshi Sato
  • Akira Namatame
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4173)


In this paper, we consider one of old-age problems about trade-off relation between homogeneity and diversity. We investigate combat based on agent-based simulation, not conventional mathematical model based on attrition. By introducing synthetic approach and adapting evolutionary learning to action rules that are expressed by a combination of parameters in combat simulation, we focus on the interaction between sets of action rules. For searching how many sets of action rules does work well, we change the number of sets of action rules. And we make statistical analysis and show that there is good intermediate stage between high homogeneity and high diversity in group.


Complex Adaptive System Action Rule Evolutionary Learn Personality Parameter Fire Range 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Tomonari Honda
    • 1
  • Hiroshi Sato
    • 1
  • Akira Namatame
    • 1
  1. 1.Dept of Computer ScienceNational Defense AcademyYokosukaJapan

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