A Methodology for Learning Players' Styles from Game Records

In Chess, as in other popular strategic board games, players have different styles. For example, in Chess some players are more "positional" and other more "tactical", and this difference in style will affect their move choice in any given board position, and more generally their overall plan. The problem we tackle in this paper is that of applying machine learning to teach a computer to discriminate between players based on their style. Before we explain our methodology, we briefly review the method of temporal difference learning, which is central to our approach. Temporal difference learning [Sut88] is a machine learning technique, originating from the seminal work of Samuel [Sam59], in which learning occurs by minimising the differences between predictions and actual outcomes of a temporal sequence of observations. Samuel [Sam59] used the game of Checkers as a vehicle to study the feasibility of a computer learning from experience. Although the program written by Samuel did not achieve master strength, it was the precursor of the Checkers program Chinook [Sch97, SHJ01], which was the first computer program to win a match against a human world champion.