Earticle

초록

영어

Evolutionary computation is a powerful tool for developing computer games. Back-propagation neural network(BPNN) was proved to be a universal approximator and genetic algorithm(GA) a global searcher. The game of Tic-Tac-Toe, also known as Naughts and Crosses, is often used as a test bed for testing new AI algorithms. We tried to recognize the strategic fitness of a finished Tic-Tac-Toe game when the parameters, such as a sequence of moves, its game depth and result, are provided. To implement this, we've constructed an evolutionary model using GA with back-propagation NNs(GANN). The experimental results revealed that GANN, in the very long training time, converges very slowly; however, performance of recognizing the strategic fitness does not meet we expected and, further, increase of the population size does not significantly contribute to the performance of GANN.

목차

ABSTRACT
 1. 서론
 2. 관련 연구
  2.1 몬테카를로 트리 탐색
  2.2 유전 알고리즘
 3. 본론
  3.1 인공망신경
  3.2 뉴런 모델
  3.3 역전파 알고리즘
  3.4 유전 알고리즘
  3.5 실험 결과
 4. 결론
 감사의 글
 참고문헌

키워드

저자

• 이병두 [ Byung-Doo Lee | 세한대학교 ] 교신저자

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  컴퓨터게임및콘텐츠논문지(구 한국컴퓨터게임학회논문지) [Journal of Computer Games and Contents]

• 간기

  월간

• pISSN

  3091-7409

• eISSN

  3092-3638

• 수록기간

  2002~2026

• 등재여부

  KCI 등재

• 십진분류

  KDC 691 DDC 793