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Two-Sided, Genetics-Based Learning to Discover Novel Fighter Combat Maneuvers

  • Robert E. Smith
  • Bruce A. Dike
  • B. Ravichandran
  • Adel El-Fallah
  • Raman K. Mehra
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2037)

Abstract

This paper reports the authors’ ongoing experience with a system for discovering novel fighter combat maneuvers, using a genetics-based machine learning process, and combat simulation. In effect, the genetic learning system in this application is taking the place of a test pilot, in discovering complex maneuvers from experience. The goal of this work is distinct from that of many other studies, in that innovation, and discovery of novelty (as opposed to optimality), is in self valuable. This makes the details of aims and techniques somewhat distinct from other genetics-based machine learning research.

This paper presents previously unpublished results that show two co-adapting players in similar aircraft. The complexities of analyzing these results, given the red queen effect are discussed. Finally, general implications of this work are discussed.

Keywords

Test Pilot Machine Learning System Complex Maneuver Machine Learning Process Real Aircraft 
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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References

  1. 1.
    R.E. Smith and B.A. Dike. Learning novel fighter combat maneuver rules via genetic algorithms. International Journal of Expert Systems, 8(3):247–276, 1995.Google Scholar
  2. 2.
    R.E. Smith, B.A. Dike, and Stegmann. Inheritance in genetic algorithms. In Proceedings of the ACM 1995 Symposium on Applied Computing, pages 345–350. ACM Press, 1994.Google Scholar
  3. 3.
    R.E. Smith, B.A. Dike, R.K. Mehra, B. Ravichandran, and A. El-Fallah. Classifier systems in combat: Two-sided learning of maneuvers for advanced fighter aircraft. Computer Methods in Applied Mechanics and Engineering, 186:421–437, 2000.CrossRefzbMATHGoogle Scholar
  4. 4.
    S.W. Wilson. Zcs: A zeroth-level classifier system. Evolutionary Computation, 2(1):1–18, 1994.MathSciNetCrossRefGoogle Scholar
  5. 5.
    P.M. Doane, C.H. Gay, and J.A. Fligg. Multi-system integrated control (music) program. Technical Report Final Report, Wright Laboratories, Wright-Patterson AFB, OH, 1989.Google Scholar
  6. 6.
    D. Floriano and S. Nolfi. God save the red queen: Competition in co-evolutionary robotics. In Proceedings of the Second International Conference on Genetic Programming, pages 398–406. MIT Press, 1997.Google Scholar
  7. 7.
    R.L. Shaw. Fighter Combat: Tactics and Maneuvering. United States Naval Institute Press, 1998.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Robert E. Smith
    • 1
  • Bruce A. Dike
    • 2
  • B. Ravichandran
    • 3
  • Adel El-Fallah
    • 3
  • Raman K. Mehra
    • 3
  1. 1.The Intelligent Computing Systems CentreBristolUK
  2. 2.The Boeing CompanySt. LouisUSA
  3. 3.Scientific SystemsWoburnUSA

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