Monte Carlo Go Has a Way to Go

Monte Carlo Go is a promising method to improve the performance of computer Go programs. This approach determines the next move to play based on many Monte Carlo samples. This paper examines the relative advantages of additional samples and enhancements for Monte Carlo Go. By parallelizing Monte Carlo Go, we could increase sample sizes by two orders of magnitude. Experimental results obtained in 9 × 9 Go show strong evidence that there are trade-offs among these advantages and performance, indicating a way for Monte Carlo Go to go.

[1]  Peter Mysliwietz.  Konstruktion und Optimierung von Bewertungsfunktionen beim Schach , 1994 .

[2]  Bruno Bouzy,et al.  Associating domain-dependent knowledge and Monte Carlo approaches within a Go program , 2005, Inf. Sci..

[3]  Ernst A. Heinz,et al.  New Self-Play Results in Computer Chess , 2000, Computers and Games.

[4]  Bruce Abramson,et al.  Expected-Outcome: A General Model of Static Evaluation , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  Bruno Bouzy,et al.  Monte-Carlo Go Developments , 2003, ACG.

[6]  Matthew L. Ginsberg,et al.  GIB: Steps Toward an Expert-Level Bridge-Playing Program , 1999, IJCAI.

[7]  Tristan Cazenave,et al.  Combining Tactical Search and Monte-Carlo in the Game of Go , 2005, CIG.

[8]  Dana S. Nau.  Pathology on Game Trees: A Summary of Results , 1980, AAAI.

[9]  Carl Ebeling,et al.  Measuring the Performance Potential of Chess Programs , 1990, Artif. Intell..

[10]  Anthony Skjellum,et al.  A High-Performance, Portable Implementation of the MPI Message Passing Interface Standard , 1996, Parallel Comput..

[11]  Jonathan Schaeffer,et al.  The challenge of poker , 2002, Artif. Intell..

[12]  SheppardBrian.  World-championship-caliber Scrabble , 2002 .

[13]  Bruno Bouzy,et al.  Associating Shallow and Selective Global Tree Search with Monte Carlo for 9*9 Go , 2004, Computers and Games.

[14]  Bruno Bouzy.  The Move-Decision Strategy of Indigo , 2003, J. Int. Comput. Games Assoc..

[15]  Martin Müller,et al.  Computer Go , 2002, Artif. Intell..

[16]  Andreas Junghanns,et al.  Diminishing Returns for Additional Search in Chess , 2007 .

[17]  Brian Sheppard,et al.  World-championship-caliber Scrabble , 2002, Artif. Intell..

[18]  Darse Billings,et al.  Search and Knowledge in Lines of Action , 2003, ACG.

[19]  Bernd Brügmann Max-Planck.  Monte Carlo Go , 1993 .

[20]  K. Thompson,et al.  COMPUTER CHESS STRENGTH , 1982 .