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Coevolutionary Ant Algorithms Playing Games

  • Jürgen Branke
  • Michael Decker
  • Daniel Merkle
  • Hartmut Schmeck
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2463)

Abstract

In this paper, we use the idea of coevolution in the context of ant algorithms to develop game-playing strategies for the simple games of Nim and Tic-Tac-Toe. Since these games have a rather small set of possible non-.nal distinct states (20 for Nim and 627 for Tic-Tac-Toe), we represent a strategyas a lookup-table, specifying for each situation the suggested move. To generate such a strategy, an ant algorithm operates on a ∣S∣ × ∣M∣ pheromone matrix, with ∣S∣ being the number of possible states, and ∣M∣ being the (maximal) number of possible moves per state. We require that the sum of pheromone values in each row is equal to 1. An ant produces a strategy by moving through all the rows and probabilistically(according to the pheromone values in each row) selecting a move for each state.

References

  1. 1.
    C. D. Rosin and R. K. Belew. New methods for competitive coevolution. Evolutionary Computation, 1996.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Jürgen Branke
    • 1
  • Michael Decker
    • 1
  • Daniel Merkle
    • 1
  • Hartmut Schmeck
    • 1
  1. 1.Institute AIFBUniversity of KarlsruheKarlsruheGermany

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