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- W2129458514 abstract "최근의 게임 플레이어들은 단순한 반복적인 조작을 벗어나 복잡한 환경 하에서 다양한 전략과 전술을 구사하여야 하는 게임을 요구하고 있다. 이러한 환경에서 게임 캐릭터를 학습시키기 위해 다양한 인공지능 기법들이 제안되었으며, 최근에는 신경망과 유전 알고리즘을 이용한 학습 방법이 연구되고 있다. 본 논문에서는 게임이론에서 널리 사용되는 추격-회피 전략의 학습을 위해 유전 프로그래밍(GP)을 사용하였다. 제안된 유전 프로그래밍은 신경망과 같은 기존의 방법에 비해 수행 속도가 빠르고, 학습의 결과를 직관적으로 이해할 수 있으며, 진화된 염색체를 추론 규칙으로 변환 가능하므로 호환성이 높다는 장점을 가지고 있다. Recently, game players want new game requiring more various tactics and strategies in the complex environment beyond simple and repetitive play. Various artificial intelligence techniques have been suggested to make the game characters learn within this environment, and the recent researches include the neural network and the genetic algorithm. The Genetic programming(GP) has been used in this study for learning strategy of the agent in the pursuit-evasion problem which is used widely in the game theories. The suggested GP algorithm is faster than the existing algorithm such as neural network, it can be understood instinctively, and it has high adaptability since the evolving chromosomes can be transformed to the reasoning rules." @default.
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- W2129458514 date "2008-06-30" @default.
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- W2129458514 title "Game Agent Learning with Genetic Programming in Pursuit-Evasion Problem" @default.
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- W2129458514 doi "https://doi.org/10.3745/kipstb.2008.15-b.3.253" @default.
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