• 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.

• 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.

• 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.

• Journal of Korea Game Society
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• v.17 no.4
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• pp.109-116
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• 2017

It takes quite amount of time to study a board game because there are many game characters and many state spaces are exist for board games. Therefore, game must do learning long. But, there is weakness with reinforcement learning. On Learning early, the learning speed becomes slow. 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 improved on learning speed.