• Title/Summary/Keyword: 줄고누

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An Improvement of the Learning Speed through Considered Distance on Jul-Gonu Game (거리를 고려한 줄고누게임의 학습속도 개선)

  • Shin, Yong-Woo;Chung, Tae-Choong
    • Journal of Korea Game Society
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    • v.10 no.1
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    • pp.105-113
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    • 2010
  • It takes quite amount of time to study a board game because there are many game characters and different stages are exist for board games. Also, the opponent is not just a single character that means it is not one on one game, but group vs. group. That is why strategy is needed, and therefore applying optimum learning is a must. If there were equal result that both are considered to be best ones during the course of learning stage, Heuristic which utilizes learning of problem area of Jul-Gonu was used to improve the speed of learning. To compare a normal character to an improved one, a jul-gonu game was created, and then they fought against each other. Improved character considered distance and attacked other one. As a result, improved character's ability was improved on learning speed.

Artificial Engine Development through Reinforcement Learning on Jul-Gonu Game (강화학습을 이용한 줄고누게임의 인공엔진개발)

  • Shin, Yong-Woo
    • Journal of Internet Computing and Services
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    • v.10 no.1
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    • pp.93-99
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    • 2009
  • Game program manufacture had been classed by 3D or on-line game etc. simply. But, atomized game programmer's kind now. So, Artificial Intelligence game programmer's role is important. This paper used reinforcement learning algorithm for Jul_Gonu board characters to learn, and so they can move intelligently. To compare a learned character to an random one, a board game was created, and then they fought against each other. As a result, learned character‘s ability was far more improved.

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An improvement of the learning speed through Improved Reinforcement Learning on Jul-Gonu Game (개선된 강화학습을 이용한 줄고누게임의 학습속도개선)

  • Shin, Yong-Woo;Chung, Tae-Choong
    • Journal of Internet Computing and Services
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    • v.10 no.3
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    • pp.9-15
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    • 2009
  • It takes quite amount of time to study a board game because there are many game characters and different stages are exist for board games. Also, the opponent is not just a single character that means it is not one on one game, but group vs. group. That is why strategy is needed, and therefore applying optimum learning is a must. This paper used reinforcement learning algorithm for board characters to learn, and so they can move intelligently. If there were equal result that both are considered to be best ones during the course of learning stage, Heuristic which utilizes learning of problem area of Jul-Gonu was used to improve the speed of learning. To compare a normal character to an improved one, a board game was created, and then they fought against each other. As a result, improved character's ability was far more improved on learning speed.

  • PDF