HAL will be down for maintenance from Friday, June 10 at 4pm through Monday, June 13 at 9am. More information
Skip to Main content Skip to Navigation
Conference papers

Computing Elo Ratings of Move Patterns in the Game of Go

Rémi Coulom 1
1 SEQUEL - Sequential Learning
LIFL - Laboratoire d'Informatique Fondamentale de Lille, Inria Lille - Nord Europe, LAGIS - Laboratoire d'Automatique, Génie Informatique et Signal
Abstract : Move patterns are an essential method to incorporate domain knowledge into Go-playing programs. This paper presents a new Bayesian technique for supervised learning of such patterns from game records, based on a generalization of Elo ratings. Each sample move in the training data is considered as a victory of a team of pattern features. Elo ratings of individual pattern features are computed from these victories, and can be used in previously unseen positions to compute a probability distribution over legal moves. In this approach, several pattern features may be combined, without an exponential cost in the number of features. Despite a very small number of training games (652), this algorithm outperforms most previous pattern-learning algorithms, both in terms of mean log-evidence (−2.69), and prediction rate (34.9%). A 19x19 Monte-Carlo program improved with these patterns reached the level of the strongest classical programs.
Document type :
Conference papers
Complete list of metadata

Cited literature [13 references]  Display  Hide  Download

https://hal.inria.fr/inria-00149859
Contributor : Rémi Coulom Connect in order to contact the contributor
Submitted on : Tuesday, May 29, 2007 - 10:44:26 AM
Last modification on : Thursday, January 20, 2022 - 4:12:30 PM
Long-term archiving on: : Thursday, April 8, 2010 - 6:06:29 PM

File

MMGoPatterns.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : inria-00149859, version 1

Collections

Citation

Rémi Coulom. Computing Elo Ratings of Move Patterns in the Game of Go. Computer Games Workshop, Jun 2007, Amsterdam, Netherlands. ⟨inria-00149859⟩

Share

Metrics

Record views

1235

Files downloads

2562