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2018 | OriginalPaper | Chapter

The Survey of Methods and Algorithms for Computer Game Go

Authors : Xiali Li, Xun Sun, Licheng Wu, Songting Deng, Qiao Gao

Published in: Proceedings of 2017 Chinese Intelligent Automation Conference

Publisher: Springer Singapore

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Abstract

Computer go game is one of the most challenging research branches in the field of artificial intelligence and cognitive science. The success of AlphaGo has received worldwide attention on deep learning and computer go. In this paper, we present the survey of methods and algorithms for computer go game searching and situation evaluation according to the discussed literature in different development stages. This paper also gives the promising future research on the computer go.

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Literature
1.
go back to reference Xu X, Deng Z (2007) Various challenging issues faced to computer game research. Progress of Artificial Intelligence in China (in Chinese) Xu X, Deng Z (2007) Various challenging issues faced to computer game research. Progress of Artificial Intelligence in China (in Chinese)
2.
go back to reference Browne CB, Powley E, Whitehouse D, Lucas SM, Cowling PI, Rohlfshagen P, Tavener S, Perez D, Samothrakis S, Colton S (2012) A survey of monte carlo tree search methods, IEEE Trans Comput Intell Ai Games 4(1):1–43 Mar 2012 Browne CB, Powley E, Whitehouse D, Lucas SM, Cowling PI, Rohlfshagen P, Tavener S, Perez D, Samothrakis S, Colton S (2012) A survey of monte carlo tree search methods, IEEE Trans Comput Intell Ai Games 4(1):1–43 Mar 2012
3.
go back to reference Zobrist AL (1969) A new hashing method with application for game playing. Tech Rep Zobrist AL (1969) A new hashing method with application for game playing. Tech Rep
4.
go back to reference Zobrist AL (1970) Feature extraction and representation for pattern recognition and the game of go. Ph.D. Thesis, 1970 Zobrist AL (1970) Feature extraction and representation for pattern recognition and the game of go. Ph.D. Thesis, 1970
5.
go back to reference Zobrist AL (1971) Complex preprocessing for pattern recognition. In: Proceeding of ACM annual conference, 1971 Zobrist AL (1971) Complex preprocessing for pattern recognition. In: Proceeding of ACM annual conference, 1971
6.
go back to reference Zhiqing L, Wenfeng L (2011) The basis of modern computer go. Beijing University of Posts and Telecommunications Publishing House (in Chinese) Zhiqing L, Wenfeng L (2011) The basis of modern computer go. Beijing University of Posts and Telecommunications Publishing House (in Chinese)
7.
go back to reference Bouzy B, Cazenave T (2001) Computer go: an Ai oriented survey. Artif Intell 132(1):39–103 Bouzy B, Cazenave T (2001) Computer go: an Ai oriented survey. Artif Intell 132(1):39–103
8.
go back to reference Campbell M, Marsland (1983) A comparison of minimax tree search algorithms. Artif Intell 1(20):347–367 Campbell M, Marsland (1983) A comparison of minimax tree search algorithms. Artif Intell 1(20):347–367
9.
go back to reference Bouzy B (2003) Associating domain-dependent knowledge and Monte Carlo approaches within a Go program. In: Proceedings of joint conference on information sciences, pp 125–136 Bouzy B (2003) Associating domain-dependent knowledge and Monte Carlo approaches within a Go program. In: Proceedings of joint conference on information sciences, pp 125–136
10.
go back to reference Bouzy B, Helmstetter (2003) Monte carlo go developments. In: Advances in Computer Games conference (ACG-10), Graz 2003, pp 159–174 Bouzy B, Helmstetter (2003) Monte carlo go developments. In: Advances in Computer Games conference (ACG-10), Graz 2003, pp 159–174
11.
go back to reference Bouzy B (2005) Move pruning techniques for Monte Carlo Go. In: Proceedings of 11th advance in Computer Game Conference, Taipei, pp 201–210 Bouzy B (2005) Move pruning techniques for Monte Carlo Go. In: Proceedings of 11th advance in Computer Game Conference, Taipei, pp 201–210
12.
go back to reference Gelly S, Wang Y (2006) Exploration exploitation in Go: UCT for Monte-Carlo go. In: Twentieth Annual Conference on Neural Information Processing Systems, Canada, pp 225–236 Gelly S, Wang Y (2006) Exploration exploitation in Go: UCT for Monte-Carlo go. In: Twentieth Annual Conference on Neural Information Processing Systems, Canada, pp 225–236
13.
go back to reference Kocsis L, Szepesvari C (2006) Bandit based monte-carlo planning. In: 15th European Conference on Machine Learning (ECML), pp 282–293 Kocsis L, Szepesvari C (2006) Bandit based monte-carlo planning. In: 15th European Conference on Machine Learning (ECML), pp 282–293
14.
go back to reference Auer P, Cesa-Bianchi N, Fischer P (2002) Finite-time analysis of the multi-armed bandit problem. Mach Learn 47(2):235–256CrossRefMATH Auer P, Cesa-Bianchi N, Fischer P (2002) Finite-time analysis of the multi-armed bandit problem. Mach Learn 47(2):235–256CrossRefMATH
15.
go back to reference He S, Wang Y, Xie F, Meng J, Chen H, Luo S, Liu Z, Zhu Q (2008) Game player strategy pattern recognition and how UCT algorithm apply pre-knowledge of player’s strategy to improve opponent AI. In: Proceedings of International Conference on Computational Intelligence Model. Control Autom, Vienna, Austria, pp 1177–1181 He S, Wang Y, Xie F, Meng J, Chen H, Luo S, Liu Z, Zhu Q (2008) Game player strategy pattern recognition and how UCT algorithm apply pre-knowledge of player’s strategy to improve opponent AI. In: Proceedings of International Conference on Computational Intelligence Model. Control Autom, Vienna, Austria, pp 1177–1181
16.
go back to reference Gelly S, Silver D (2007) Combining online and offline learning in UCT. In: 24th International Conference on Machine Learning, pp 273–280 Gelly S, Silver D (2007) Combining online and offline learning in UCT. In: 24th International Conference on Machine Learning, pp 273–280
17.
go back to reference Rémi Coulom (2006) Efficient selectivity and backup operators in Monte-Carlo tree search. Submitted to CG 2006, 2006 Rémi Coulom (2006) Efficient selectivity and backup operators in Monte-Carlo tree search. Submitted to CG 2006, 2006
18.
go back to reference Helmbold DP, Parker-Wood A (2009) All-moves-as-first heuristics in Monte-Carlo Go. In: Proceedings of International Conference on Artificial Intelligence, Las Vegas, NV, pp 605–610 Helmbold DP, Parker-Wood A (2009) All-moves-as-first heuristics in Monte-Carlo Go. In: Proceedings of International Conference on Artificial Intelligence, Las Vegas, NV, pp 605–610
19.
go back to reference Gelly S, Silver D (2011) Monte-Carlo tree search and rapid action value estimation in Computer Go. Artif Intell 175:1856–1875CrossRefMathSciNet Gelly S, Silver D (2011) Monte-Carlo tree search and rapid action value estimation in Computer Go. Artif Intell 175:1856–1875CrossRefMathSciNet
20.
go back to reference Chen K-H (2012) Dynamic randomization and domain knowledge in Monte-Carlo tree search for Go knowledge-based systems, knowledge based systems, vol 34, pp 21–25 Chen K-H (2012) Dynamic randomization and domain knowledge in Monte-Carlo tree search for Go knowledge-based systems, knowledge based systems, vol 34, pp 21–25
21.
go back to reference Xiali L, Licheng W (2015) A multi-modal searching algorithm in computer go based on test, CIAC2015, Lecture Notes in Electrical Engineering of Springer, Fuzhou, China, pp 68–72 Xiali L, Licheng W (2015) A multi-modal searching algorithm in computer go based on test, CIAC2015, Lecture Notes in Electrical Engineering of Springer, Fuzhou, China, pp 68–72
22.
go back to reference Silver D, Huang A, Maddison CJ et al. (2016) Mastering the game of Go with deep neural networks and tree search. Nature 529(7587):484–489 Silver D, Huang A, Maddison CJ et al. (2016) Mastering the game of Go with deep neural networks and tree search. Nature 529(7587):484–489
23.
go back to reference Tian Y, Zhu Y (2015) Better computer go player with neural network and long-term prediction. Comput Sci Tian Y, Zhu Y (2015) Better computer go player with neural network and long-term prediction. Comput Sci
Metadata
Title
The Survey of Methods and Algorithms for Computer Game Go
Authors
Xiali Li
Xun Sun
Licheng Wu
Songting Deng
Qiao Gao
Copyright Year
2018
Publisher
Springer Singapore
DOI
https://doi.org/10.1007/978-981-10-6445-6_29