TY - GEN
T1 - Monte-Carlo simulation balancing in practice
AU - Huang, Shih Chieh
AU - Coulom, Rémi
AU - Lin, Shun Shii
N1 - Funding Information:
We thank David Silver for his comments and encouragements.We are also grateful to Lin Chung-Hsiung for kindly providing access to the game database of the web2go web site. Hardware was provided by project NSC98-2221-E-003-013 from National Science Council, R.O.C. This work was supported in part by the IST Programme of the European Community, under the PASCAL2 Network of Excellence, IST-2007-216886. This work was supported in part by Ministry of Higher Education and Research, Nord-Pas de Calais Regional Council and FEDER through the “CPER 2007-2013”. This publication only reflects the authors’ views.
Funding Information:
We thank David Silver for his comments and encouragements. We are also grateful to Lin Chung-Hsiung for kindly providing access to the game database of the web2go web site. Hardware was provided by project NSC98-2221-E-003-013 from National Science Council, R.O.C. This work was supported in part by the IST Programme of the European Community, under the PASCAL2 Network of Excellence, IST-2007-216886. This work was supported in part by Ministry of Higher Education and Research, Nord-Pas de Calais Regional Council and FEDER through the “CPER 2007–2013”. This publication only reflects the authors’ views.
PY - 2011
Y1 - 2011
N2 - Simulation balancing is a new technique to tune parameters of a playout policy for a Monte-Carlo game-playing program. So far, this algorithm had only been tested in a very artificial setting: it was limited to 5x5 and 6x6 Go, and required a stronger external program that served as a supervisor. In this paper, the effectiveness of simulation balancing is demonstrated in a more realistic setting. A state-of-the-art program, Erica, learned an improved playout policy on the 9x9 board, without requiring any external expert to provide position evaluations. The evaluations were collected by letting the program analyze positions by itself. The previous version of Erica learned pattern weights with the minorization-maximization algorithm. Thanks to simulation balancing, its playing strength was improved from a winning rate of 69% to 78% against Fuego 0.4.
AB - Simulation balancing is a new technique to tune parameters of a playout policy for a Monte-Carlo game-playing program. So far, this algorithm had only been tested in a very artificial setting: it was limited to 5x5 and 6x6 Go, and required a stronger external program that served as a supervisor. In this paper, the effectiveness of simulation balancing is demonstrated in a more realistic setting. A state-of-the-art program, Erica, learned an improved playout policy on the 9x9 board, without requiring any external expert to provide position evaluations. The evaluations were collected by letting the program analyze positions by itself. The previous version of Erica learned pattern weights with the minorization-maximization algorithm. Thanks to simulation balancing, its playing strength was improved from a winning rate of 69% to 78% against Fuego 0.4.
UR - http://www.scopus.com/inward/record.url?scp=79952027913&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=79952027913&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-17928-0_8
DO - 10.1007/978-3-642-17928-0_8
M3 - Conference contribution
AN - SCOPUS:79952027913
SN - 3642179274
SN - 9783642179273
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 81
EP - 92
BT - Computers and Games - 7th International Conference, CG 2010, Revised Selected Papers
PB - Springer Verlag
ER -