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Two-ply iterative deepening in Chinese-chess computer game

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4 Author(s)
Xi-Zhao Wang ; Key Lab. for Machine Learning & Comput. Intell., Hebei Univ., Baoding, China ; Yu-Lin He ; Pan Su ; Wen-Liang Li

In Chinese-chess computer game (CCCG), a computer player could find the best move for a given board position by using alpha-beta search algorithm. The technique of iterative deepening is an enhancement to alpha-beta search. It is helpful to reduce the size of game tree. In this paper, we improved the prototypical one-ply iterative deepening (OPID) and proposed two-ply iterative deepening (TPID). In game tree searching, we extend the search by two plies from the previous iteration. An iterated series of 2-ply, 4-ply, 6-ply, --- searches is carried out. In the experiments, we validate that TPID is feasible and effective. Through applying TPID to minimax search and alpha-beta search respectively, we found that the total number of nodes generated in TPID minimax search and TPID alpha-beta search are all reduced compared with OPID.

Published in:

Machine Learning and Cybernetics, 2009 International Conference on  (Volume:4 )

Date of Conference:

12-15 July 2009