• Journal of KIISE:Computer Systems and Theory
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• v.37 no.2
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• pp.66-75
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• 2010

This paper proposes an efficient method for design and implementation of the artificial intelligence (AI) of 'omok' game on the GPU. The proposed AI is designed on a cooperative structure using min-max game tree and genetic algorithm. Since the evaluation function needs intensive computation but is independently performed on a lot of candidates in the solution space, it is computed on the GPU in a massive parallel way. The implementation on NVIDIA CUDA and the experimental results show that it outperforms significantly over the CPU, in which parallel game tree and genetic algorithm on the GPU runs more than 400 times and 300 times faster than on the CPU. In the proposed cooperative AI, selective search using genetic algorithm is performed subsequently after the full search using game tree to search the solution space more efficiently as well as to avoid the thread overflow. Experimental results show that the proposed algorithm enhances the AI significantly and makes it run within the time limit given by the game's rule.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.530-532
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• 2021

This paper deals with AI education service that enables developers who have completed basic programming education to program in C/C++ language in order to learn big data and artificial intelligence. In addition, a customized development environment configuration system according to the development environment and how the user implements and tests artificial intelligence are explained. And also it has a function to check the effect on artificial intelligence through manipulation of various internal parameters. It is expected that it will be possible to develop artificial intelligence education services without language restrictions through networks in the future.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.05a
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• pp.618-620
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• 2022

게임은 수많은 NPC 와 규칙에 의해 작동되는 가상 공간을 의미한다. 이런 가상 공간에서는 규칙을 엄격히 지키면서 수행되는 AI 를 필수로 요구하게 된다. 하지만 강화 학습 기반의 AI 는 복잡한 게임의 규칙을 온전히 지키지 못하고 예상 밖의 행동을 돌출하면서 이를 해결하기 위한 많은 연구도 수행되고 있다. 본 논문에서는 규칙 기반으로 획득한 오목판의 확률 맵과 학습을 통해 획득한 확률맵 데이터를 병합하여 가장 높은 Value 를 가지는 위치를 다음 수로 반환하는 방법을 사용하였다. 향후 연구에서는 ANN(Approximate Nearest Neighbor)알고리즘을 적극 활용하여, 커널의 State 와 보드의 State 비교를 확률적으로 개선할 예정이다. 본 논문에서 제안된 프레임 워크는 게임 AI 연구에 기여할 수 있길 바란다.