Localizing Search in Monte-Carlo Go using Statistical Covariance

Article type: Research Article

Authors: Pellegrino, Seth¹ | Hubbard, Andrew² | Galbraith, Jason³ | Drake, Peter⁴ | Chen, Yung-Pin⁵

Affiliations: Lewis & Clark College, Portland, Oregon

Note: [1] email: [email protected]

Note: [2] email: [email protected]

Note: [3] email: jason [email protected]

Note: [4] email: [email protected]

Note: [5] email: [email protected]

Abstract: The classical Asian game of Go presents a difficult challenge for artificial intelligence programmers because of its vast search space and the difficulty of static board evaluation. One of the techniques humans employ while playing Go is spatial localization – they direct their attention toward the important points on the board and ignore the unimportant areas. This note describes using statistical covariance as a tool to execute spatial localization in our Go program, OREGO.

DOI: 10.3233/ICG-2009-32305

Journal: ICGA Journal, vol. 32, no. 3, pp. 154-160, 2009