Artificial Intelligence - Chips challenging champions: games, computers and Artificial Intelligence
Combining online and offline knowledge in UCT
Proceedings of the 24th international conference on Machine learning
Efficient selectivity and backup operators in Monte-Carlo tree search
CG'06 Proceedings of the 5th international conference on Computers and games
Bandit based monte-carlo planning
ECML'06 Proceedings of the 17th European conference on Machine Learning
On the scalability of parallel UCT
CG'10 Proceedings of the 7th international conference on Computers and games
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
A lock-free multithreaded monte-carlo tree search algorithm
ACG'09 Proceedings of the 12th international conference on Advances in Computer Games
Evaluation of a simple, scalable, parallel best-first search strategy
Artificial Intelligence
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Monte-Carlo Tree Search is a powerful paradigm for the game of Go. In this contribution we present a parallel Master-Slave algorithm for Monte-Carlo Tree Search and test it on a network of computers using various configurations: from 12,500 to 100,000 playouts, from 1 to 64 slaves, and from 1 to 16 computers. On our own architecture we obtain a speedup of 14 for 16 slaves. With a single slave and five seconds per move our algorithm scores 40.5% against GNU Go, with sixteen slaves and five seconds per move it scores 70.5%. At the end we give the potential speedups of our algorithm for various playout times.