한국게임학회 논문지 (Journal of Korea Game Society)

  • 제21권6호
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  • Pages.31-40
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  • 2021
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  • 1598-4540(pISSN)
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  • 2287-8211(eISSN)
한국게임학회 (Korea Game Society)
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몬테카를로 방법과 점 패턴 매칭을 활용한 바둑에서의 사활문제 해결을 위한 원형 안형의 분류

Prototypical Eye Shape Classification to Solve Life-and-Death Problem in Go, using Monte-Carlo Method and Point Pattern Matching

  • Lee, Byung-Doo (School of Artificial Intelligence, Yong In University)
  • 투고 : 2021.09.17
  • 심사 : 2021.10.30
  • 발행 : 2021.12.20
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⟨ 이전 논문 다음 논문 ⟩

초록

바둑은 2,500년 이상의 역사를 지녔고, 바둑에서의 사활문제는 컴퓨터 바둑을 구축 시에 반드시 해결해야 되는 기본 문제영역이 된다. 본 논문에서는 사활문제와 직결되는 3, 4, 5, 6궁에 대한 원형 안형의 개수 확인과 4-튜플 형식으로 표현된 원형 안형을 분류하고자 했다. 실험은 몬테카를로 방법과 점 패턴 매칭에 의해 수행되었다. 실험 결과에 따르면 원형 안형의 개수는 3궁 2개, 4궁 5개, 5궁 12개, 6궁 35개가 된다. 아울러 4-튜플 형식으로 된 원형 안형을 분류하면 3궁 1가지, 4궁 3가지, 5궁 4가지, 6궁 8가지로 분류된다.

Go has a history of more than 2,500 years, and the life-and-death problems in Go is a fundamental problem domain that must be solved when implementing a computer Go. We attempted to determine the numbers of prototypical eye shapes with 3, 4, 5, and 6 eyes that are directly related to the life-and-death problems, and to classify the prototypical eye shapes represented in 4-tuple forms. Experiment was conducted by Monte-Carlo method and point pattern matching. According to the experimental results, the numbers of prototypical eye shapes were 2 for 3-eye, 5 for 4-eye, 12 for 5-eye, and 35 for 6-eye shapes. Further, using a 4-tuple form, we classified prototypical eye shapes into 1 for 3-eye, 3 for 4-eye, 4 for 5-eye, and 8 for 6-eye shapes.

키워드

참고문헌

  1. Wikipedia, "Life and death", https://en.wikipedia.org/wiki/Life_and_death, September, 2021.
  2. B. D. Lee and Y. W. Keum, "Candidate First Move for Solving Life-and-Death Problems in the Game of Go" Journal of Korea Game Society, Vol. 9, No. 1, pp.105-114, 2009.
  3. D. B. Benson, "Life in the game of Go" Information Intelligence 10, Vol. 9, No. 1, 91-124, 1976.
  4. K. Chen and Z. Chen, "Static analysis of life and death in the game of Go" Journal of Information Sciences 121, 113-134, 1999. https://doi.org/10.1016/S0020-0255(99)00083-3
  5. N. Sasaki, Y. Sawada, J. Yoshimura, "A Neural Network Program of Tsume-Go" Computer and Games 1998, pp.167-182, Springer-Verlag, Germany, 1999.
  6. B. D. Lee, "Multi-strategic Learning, Reasoning and Searching in the Game of Go", PhD thesis, University of Auckland, New Zealand, 2005.
  7. D. Silver, et al., "Mastering the game of Go with deep neural networks and tree search" Journal of Nature, Vol. 529, Issue 7587, pp.484-489, 2016. https://doi.org/10.1038/nature16961
  8. T. Cazenave and B. Helmsetter, "Combining tactical search and deep learning in the game of Go", In: CIG'05, pp.171-175, 2005.
  9. B. H. Shekar and B. Pilar, "Shape Representation and Classification through Pattern Spectrum and Local Binary Pattern" 2014 Fifth International Conference on Signal and Image Processing, 2014.
  10. M. A. Akta, "Shape Descriptors", PhD thesis, University of Exter, England, 2012.
  11. M. Tico and C. Rusu, "Point Pattern Matching using a Genetic Algorithm and Voronoi Tessellation" 9th European Signal Processing Conference (EUSIPCO 1998), pp. 1-4, 1998.
  12. W. Xiaoyun and Z. Xianquan, "Point Pattern Matching Algorithm for Planar Point Sets under Euclidean Transform" Journal of Applied Mathematics, Vol. 2012, 2012.
  13. Wikipedia, "Hausdorff distance", https://en.wikipedia.org/wiki/Hausdorff_distance, September, 2021.
  14. D. Aiger and K. Kedem, "Approximate input sensitive algorithms for point pattern matching", Pattern Recognition 43, pp.153-159, 2010. https://doi.org/10.1016/j.patcog.2009.05.014
  15. Programiz, "Depth First Search (DFS)", https://www.programiz.com/dsa/graph-dfs, September, 2021.