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The Role of Behavioral Diversity and Difficulty of Opponents in Coevolving Game-Playing Agents


[ 1 ] Instytut Informatyki, Wydział Informatyki, Politechnika Poznańska | [ P ] employee

Year of publication


Chapter type


Publication language


  • behavioral diversity
  • diversity maintenance
  • test difficulty
  • competitive coevolution
  • generalization performance
  • games
  • Othello

EN Generalization performance of learning agents depends on the training experience to which they have been exposed. In game-playing domains, that experience is determined by the opponents faced during learning. This analytical study investigates two characteristics of opponents in competitive coevolutionary learning: behavioral diversity and difficulty (performance against other players). To assess diversity, we propose a generic intra-game behavioral distance measure, that could be adopted to other sequential decision problems. We monitor both characteristics in two-population coevolutionary learning of Othello strategies, attempting to explain their relationship with the generalization performance achieved by the evolved solutions. The main observation is the existence of a non-obvious trade-off between difficulty and diversity, with the latter being essential for obtaining high generalization performance.

Pages (from - to)

394 - 405





Applications of Evolutionary Computation : 18th European Conference, EvoApplications 2015, Copenhagen, Denmark, April 8-10, 2015 : Proceedings

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18th European Conference on the Applications of Evolutionry Computation, EvoApplications 2015, 8-10.04.2015, Copenhagen, Denmark

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