Using Chunking to Optimise an Alpha-Beta Search

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.