Synonyms
Definition
Computer Go was an interesting target in AI domain because Go was exceptionally difficult for computers among popular two-player zero-sum games.
Overview
As widely known, computers are now superior to human beings in most of the popular two-player zero-sum perfect information games including checkers, chess, shogi, and Go. The minimax search-based approach is known to be effective for most games in this category. Since Go is also one of such games, intuitively minimax search should also work for Go. However, despite the simple rules which had changed only slightly in these 2,000 years, Go is arguably the last two-player zero-sum game in which human beings are still superior to computers.
The solution to the difficulty of Go was a combination of random sampling and search. The resulting algorithm, Monte Carlo tree search (MCTS), was not only a major breakthrough for computer Go but also an important invention for many other domains...
This is a preview of subscription content, log in via an institution to check access.
References
Auer, P., Cesa-Bianchi, N., Fischer, P.: Finite-time analysis of the multi-armed bandit problem. Mach. Learn. 47, 235–256 (2002)
Berlekamp, E., Wolfe, D.: Mathematical go: chilling gets the last point. A K Peters, Wellesley (1994)
Brügmann, B. Monte Carlo Go. Technical report, 1993. Unpublished draft, http://www.althofer.de/Bruegmann-MonteCarloGo.pdf
Coulom, R.: Efficient selectivity and backup operators in Monte-Carlo tree search. In: Proceedings of the 5th International Conference on Computers and Games (CG’2006). Lecture Notes in Computer Science, vol. 4630, pp. 72–83 (2006)
Gelly, S., Wang, Y., Munos, R., Teytaud, O.: Modification of UCT with patterns in Monte-Carlo Go. Technical report 6062, INRIA (2006)
Gelly, S., Silver, D.: Combining online and offline knowledge in UCT. In: Proceedings of the 24th International Conference on Machine Learning (ICML 2007), pp. 273–280 (2007)
Kgs go server. https://www.gokgs.com/ . Accessed 12 Feb 2015
Kocsis, L., Szepesvári, C.: Bandit based Monte-Carlo planning. In: Proceedings 17th European Conference on Machine Learning (ECML 2006), pp. 282–293 (2006)
Lai, T.L., Robbins, H.: Asymptotically efficient adaptive allocation rules. Adv. Appl. Math. 6(1), 4–22 (1985)
Müller, M.: Computer Go. Artif. Intell. 134(1–2), 145–179 (2002)
Robson, J.M.: The complexity of go. In: IFIP Congress, pp. 413–417 (1983)
Schaeffer, J., Müller, M., Kishimoto, A.: Ais have mastered chess. will go be next? IEEE Spectrum, July 2014.
van der Werf, E.C.D.: First player scores for mxn go. http://erikvanderwerf.tengen.nl/mxngo.html . Accessed Dec 2015
Wedd, N.: Human-computer go challenges. http://www.computer-go.info/h-c/index.html . Accessed 12 Feb 2015
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this entry
Cite this entry
Yoshizoe, K., Müller, M. (2015). Computer Go. In: Lee, N. (eds) Encyclopedia of Computer Graphics and Games. Springer, Cham. https://doi.org/10.1007/978-3-319-08234-9_20-1
Download citation
DOI: https://doi.org/10.1007/978-3-319-08234-9_20-1
Received:
Accepted:
Published:
Publisher Name: Springer, Cham
Online ISBN: 978-3-319-08234-9
eBook Packages: Springer Reference Computer SciencesReference Module Computer Science and Engineering