TY - GEN
T1 - Two-ply iterative deepening in Chinese-chess computer game
AU - WANG, Xi-Zhao
AU - HE, Yu-Lin
AU - SU, Pan
AU - LI, Wen-Liang
N1 - This research is supported by the Natural Science Foundation of Hebei Province (F2008000635), by the key project foundation of applied fundamental research of Hebei Province (08963522D), by the plan of 100 excellent innovative scientists of the first group in Education Department of Hebei Province, and by the Scientific Research Foundation of Hebei Province (06213548).
PY - 2009
Y1 - 2009
N2 - 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.
AB - 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.
KW - Alpha-beta search algorithm
KW - Chinese chess computer game
KW - Game tree
KW - Minimax search
KW - One-ply iterative deepening
KW - Two-ply iterative deepening
UR - http://www.scopus.com/inward/record.url?scp=70350712442&partnerID=8YFLogxK
U2 - 10.1109/ICMLC.2009.5212141
DO - 10.1109/ICMLC.2009.5212141
M3 - Conference paper (refereed)
AN - SCOPUS:70350712442
SN - 9781424437023
T3 - International Conference on Machine Learning and Cybernetics (ICMLC)
SP - 2020
EP - 2026
BT - Proceedings of the 2009 International Conference on Machine Learning and Cybernetics
PB - IEEE
T2 - 2009 International Conference on Machine Learning and Cybernetics
Y2 - 12 July 2009 through 15 July 2009
ER -