Abstract
The efficiency of the alpha-beta algorithm is largely dependent on the order in which its branches are searched; a well-ordered search can give a considerable reduction in the number of nodes processed by pruning ineffectual paths. This paper describes ‘CLAMP’ (an acronym for Chunk Learning And Move Prompting) which uses ‘chunk knowledge’ to order the moves on a chessboard in their likelihood to be played. Test results show, despite CLAMP having no knowledge of the rules of chess, ordering moves by using chunk knowledge gives an approximate 50% decrease in the number of nodes searched when compared to a random ordering of the same moves. This paper focuses on the alpha-beta function within a chess-playing program but as CLAMP has no knowledge of the rules of the game the same method can be applied to optimise searching in other domains.