Co-evolving Soccer Softbot Team Coordination with Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{luke:1997:csstcGP,
  author =       "Sean Luke and Charles Hohn and Jonathan Farris and 
                 Gary Jackson and James Hendler",
  title =        "Co-evolving Soccer Softbot Team Coordination with
                 Genetic Programming",
  booktitle =    "Proceedings of the First International Workshop on
                 RoboCup, at the International Joint Conference on
                 Artificial Intelligence",
  year =         "1997",
  address =      "Nagoya, Japan",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.cs.gmu.edu/~sean/papers/robocupc.pdf",
  URL =          "http://www.cs.gmu.edu/~sean/papers/robocupc.ps.gz",
  size =         "4 pages",
  abstract =     "Genetic Programming is a promising new method for
                 automatically generating functions and algorithms
                 through natural selection. In contrast to other
                 learning methods, Genetic Programming's automatic
                 programming makes it a natural approach for developing
                 algorithmic robot behaviors. In this paper we present
                 an overview of how we apply Genetic Programming to
                 behavior-based team coordination in the RoboCup Soccer
                 Server domain. The result is not just a hand-coded
                 soccer algorithm, but a team of softbots which have
                 learned on their own how to play a reasonable game of
                 soccer.",
  notes =        "IJCAI-97

                 Given the acknowledged challenges of applying Genetic
                 Programming to robot soccer, we were happy to just show
                 up at Nagoya with an entry in the RoboCup simulation
                 track. However, Maryland's Genetic Programming entry in
                 in fact beat its first two competitors (5-2 against U
                 British Columbia, Canada and 17-0 over Toyohashi
                 University of Science and Technology, Japan) before
                 losing to University of Tokyo (last year's champion,
                 6-1) and subsequently Tokyo Institute of Technology
                 (16-4) in the single-elimination round. For its
                 research achievement in demonstrating the feasibility
                 of evolutionary computation in a very difficult domain,
                 Maryland's entry also won the RoboCup Scientific
                 Challenge Award.
                 http://ci.etl.go.jp/~noda/soccer/RoboCup97/result.html
                 Part of Email from John Koza Fri, 29 Aug 1997 21:37:50
                 PDT to genetic-programming@cs.stanford.edu {"}The
                 Maryland entry competed against various hand-written
                 robot controllers (all of which are very good examples
                 of clever human programming) and its success
                 demonstrated, I think, that GP is precisely the right
                 way to create programmers when the task really gets
                 difficult. {"}

                 Too short to give full technical details: STGP, 50ish
                 problem dependant functions. team composed of 2-3
                 squads of identical players. Each squad 2 trees (used
                 for possetion and non-possetion of ball. 6 or 12 trees
                 per GP indivdual. Co-evolution. lil-gp. Stepped
                 evolution (like seeding?) build squad from good
                 players, team from good squads.",
}

Genetic Programming entries for Sean Luke Charles Hohn Jonathan Farris Gary Jackson James A Hendler

Citations