Computer Go – Scholarly Papers & Bibliographies

• Markus Enzenberger's Online Computer Go Bibliography.

• Jay Scott's online papers on Machine Learning in Games.

• Yellow Bishop's computer chess bibliography is likely to be of interest to computer go researchers.

• Commented Papers on Go Programming from the OpenGo site.

• By Martin Müller:
  • His list of his own go articles
  • His thesis
  • His list of over 100 references

• "Best Play for Imperfect Players and Game Tree Search", by Eric B. Baum and Warren D. Smith, 1995.
  Standard alpha-beta searches assume that the evaluation function is correct. This paper takes a Bayesian approach to the values it generates.

• "A New Computational Approach to the Game of Go", by J. Churchill, R. Cant and D. Al-Dabass, 2001.

• Approaching Go as an abstract graph problem goes back a long way, to K. J. Friedenbach's PhD thesis, "Abstraction Hierarchies: A Model of Perception and Cognition in the Game of Go", University of California, Santa Cruz, 1980.

• Tim Klinger's thesis, titled "Adversarial Reasoning: A Logical Approach for Computer Go", available for download from his homepage (under Research Interests). Tim notes that "This is mostly about work that I did with David Mechner on a knowledge-based life and death problem solver. It uses a logical theory of life and death (expressed in a modal logic) coupled with pattern knowledge about "reasonable" moves to solve uncircumscribed, beginner life and death problems (from Kano I and II). There's also some discussion of the logic itself and a formalization of some basic go concepts and rules."

• "Analyzing Capturing Races and Seki Situations in the Game of GO by Semeai Graphs", by Katsuhiko Nakamura, Tokyo Denki University, Saitama, Japan, given at ACC '99 9th Conference Advances in Computer Chess, to be published in "Advances in Computer Chess 9".

• "Applying Adversarial Planning Techniques to Go", Willmott, S., Richardson, J. D. C., Bundy, A., Levine, J. M., Journal of Theoretical Computer Science, 252 (1-2) (2001) pp. 45-82.

• B* Probability Based Search is an alternative to minimax searching with possible application to computer go; it is described in a paper by Hans J. Berliner and Chris McConnell, Artificial Intelligence 86(1): 97-156 (1996)).

• Benson's seminal paper describing his life and death algorithm.

• Thomas Thomsen. Lambda-search in game trees – with application to Go, Computers and Games 2000, LNCS (Springer).

• Thore Graepel, Mike Goutrie, Marco Krüger, and Ralf Herbrich. Learning on Graphs in the Game of Go, submitted to ICANN 2001.

• Not an academic paper, but a eye-catching poster that is an effective introduction to the concepts of Computer Go: "Machine Learning Applied to the Game of Go", by Cook, H., Venghaus, A., & Drake, P. (2003). Twelfth Regional Conference on Undergraduate Research, Murdock College Research Program.

• "Modelling Uncertainty in the Game of Go", by David Stern, Thore Graepel and David MacKay.

• Temporal difference learning for game players. The original work was by Gerald Tesauro to train a backgammon player.

  Others thought it might be a nice technique to apply to other games, such as checkers or GO. Chellapilla and Fogel trained checkers players using techniques similar to those used by Tesauro for his backgammon player. See here; (Look for Chellapilla and Fogel, "Co-evolving checkers playing programs using only win, lose or draw", from SPIE 1999.

  There has also been some work done on applying similar techniques to GO players, such as Nici Schraudolphs work on applying temporal difference learning neural networks to GO, found here.

  Another reference is the work from J. Baxter et al. applied to the game of chess. Check out "TDLeaf(Lambda): Combining temporal difference learning with game-tree search", available here.

• "Using Hard And Soft Artificial Intelligence Algorithms To Simulate Human Go Playing Techniques", by R. Cant, J. Churchill and D. Al-Dabass, Int. J. of Simulation, Vol. 2, No.1, June 2001, pp 31-49.

• Computer Go "An international bulletin devoted to the generation and exchange of ideas about Computer Go", edited by David Erbach. Quarterly, from 1986-1991. A complete set of 16 back issues, in pdf format.

• On the ICGA web site:
  • Chen, K.-H. (2000). Some Practical Techniques for Global Search in Go. Vol. 23, No. 2, pp. 67-74.
  • Thomsen, T. (2000). Lambda-Search in Game Trees — with Application to Go. Vol. 23, No. 4, pp. 203-217.
  • Chen, K.-H. (2001). Computer Go: Knowledge, Search, and Move Decision. Vol. 24, No. 4, pp. 203-215.

• The MSRI website contains Bill Spight, Analysis of the 4/21/98 Jiang-Rui endgame, and Bill Fraser, Analysis Tools: "Brute-Force" and "Winsolve".