  We describe a preliminary investigation into learning a Chess player's style
from game records. The method is based on attempting to learn features of a
player's individual evaluation function using the method of temporal
differences, with the aid of a conventional Chess engine architecture. Some
encouraging results were obtained in learning the styles of two recent Chess
world champions, and we report on our attempt to use the learnt styles to
discriminate between the players from game records by trying to detect who was
playing white and who was playing black. We also discuss some limitations of
our approach and propose possible directions for future research. The method we
have presented may also be applicable to other strategic games, and may even be
generalisable to other domains where sequences of agents' actions are recorded.
