Learning Logical Exceptions In Chess

This thesis is about inductive learning, or learning from examples. The goal has been to investigate ways of improving learning algorithms. The chess end-game "King and Rook against King" (KRK) was chosen, and a number of benchmark learning tasks were defined within this domain, sufficient to over-challenge stateof -the-art learning algorithms. The tasks comprised learning rules to distinguish (1) illegal positions and (2) legal positions won optimally in a fixed number of moves. From our experimental results with task (1) the best-performing algorithm was selected and a number of improvements were made. The principal extension to this generalisation method was to alter its representation from classical logic to a non-monotonic formalism. A novel algorithm was developed in this framework to implement rule specialisation, relying on the invention of new predicates. When experimentally tested this combined approach did not at first deliver the expected performance gains due to restrictio..