DocumentCode
1748884
Title
A parallel computer-Go player, using HDP method
Author
Cai, Xindi ; Wunsch, Donald C., II
Author_Institution
Dept. of Electr. & Comput. Eng., Missouri Univ., Rolla, MO, USA
Volume
4
fYear
2001
fDate
2001
Firstpage
2373
Abstract
The game of Go has simple rules to learn but requires complex strategies to play well, and, the conventional tree search algorithm for computer games is not suited for Go program. Thus, the game of Go is an ideal problem domain for machine learning algorithms. This paper examines the performance of a 19×19 computer Go player, using heuristic dynamic programming (HDP) and parallel alpha-beta search. The neural network based Go player learns good Go evaluation functions and wins about 30% of the games in a test series on 19×19 board
Keywords
dynamic programming; games of skill; learning (artificial intelligence); neural nets; parallel processing; search problems; Go game; alpha-beta search; evaluation functions; heuristic dynamic programming; learning algorithms; neural network; parallel search; Books; Computational intelligence; Concurrent computing; Dynamic programming; Laboratories; Law; Legal factors; Machine learning algorithms; Neural networks; Testing;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 2001. Proceedings. IJCNN '01. International Joint Conference on
Conference_Location
Washington, DC
ISSN
1098-7576
Print_ISBN
0-7803-7044-9
Type
conf
DOI
10.1109/IJCNN.2001.938737
Filename
938737
Link To Document