Acquisition of go knowledge from game records
Takuya Kojima, Atsushi Yoshikawa · 2001
A large amount of knowledge is considered very useful for systems playing such games as Go and shogi (Japanese chess) which have a much larger search space than chess. Knowledge acquisition is therefore necessary for systems playing these games. This paper explains two approaches to acquire Go knowledge from game records: a deductive approach and an evolutionary one. The former is taken to acquire strict knowledge; several rules are acquired from a single training example. The latter is taken to acquire a large amount of heuristic knowledge from a large amount of training examples. Tsume-go problems are solved in order to compare the performance of the algorithm with others.