Advertisement

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)

Abstract

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.

Keywords

Complex Adaptive System Action Rule Evolutionary Learn Personality Parameter Fire Range 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Ilachinski, A.: Artificial War: Multiagent-Based Simulation of combat, World Science (2004)Google Scholar
  2. 2.
    Yang, A., Abbass, H.A., Sacker, R., Barlow, M.: Network Centric Multi-Agent Systems: A Novel Architecture, TR-ALAR-200504004 (2005)Google Scholar
  3. 3.
    Lauren, M.K.: Firepower Concentration in Cellular Automata Models –An Alternative to the Lanchester Approach, DOTSE Report 172, NR 1350. Defence Operational Technology Support Establishment, New Zealand (2000)Google Scholar
  4. 4.
    Lauren, M.K.: Fractal Methods Applied to Describe Cellular Automaton Combat Models. Fractal 9(2) (2002)Google Scholar
  5. 5.
    Davis, L.: Handbook Of Genetic Algorithms. International Thomson Computer Press (1991)Google Scholar
  6. 6.
    Mandelbrot, B.B.: Fractals and Scaling in Finance. Springer, New York (1997)MATHGoogle Scholar

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

Personalised recommendations