Generalising Closed World Specialisation: A Chess End Game Application
BibTeX
@MISC{Bain_generalisingclosed,
author = {Michael Bain and Stephen Muggleton and Ashwin Srinivasan},
title = {Generalising Closed World Specialisation: A Chess End Game Application},
year = {}
}
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Abstract
The chess endgame domain provides a continuing challenge as a benchmark for the study of concept learning. The King and Rook against King (KRK) endgame is simple in chess terms but provides concept learning tasks which can be demanding, as evidenced in previous studies by a number of authors. For learning systems these tasks have highlighted problems of representation, such as the ability to express the structural relationships to be found in training examples, and other issues like correctness, compression and comprehensibility. Our focus is on Inductive Logic Programming (ILP) methods which are based on the generalisation and specialisation of normal logic programs. We are principally concerned with improving these methods to be able to handle more examples during learning. The main contribution consists of a new method of incremental learning, together with new results on a set of concept learning problems taken from the KRK endgame domain. 1 Introduction In recent years a theme h...
Keyphrases
chess end game application world specialisation recent year krk endgame domain previous study new method chess term incremental learning normal logic program concept learning inductive logic programming structural relationship new result training example main contribution chess endgame domain
