Expected-Outcome: A General Model of Static Evaluation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Technical Note: \cal Q-Learning
Machine Learning
Artificial Intelligence
Computer Go: an AI oriented survey
Artificial Intelligence
Artificial Intelligence - Chips challenging champions: games, computers and Artificial Intelligence
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Monte Carlo go has a way to go
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Virtual global search: application to 9×9 Go
CG'06 Proceedings of the 5th international conference on Computers and games
Monte-Carlo simulation balancing in practice
CG'10 Proceedings of the 7th international conference on Computers and games
Monte-Carlo tree search and rapid action value estimation in computer Go
Artificial Intelligence
Adding expert knowledge and exploration in monte-carlo tree search
ACG'09 Proceedings of the 12th international conference on Advances in Computer Games
Move-Pruning techniques for monte-carlo go
ACG'05 Proceedings of the 11th international conference on Advances in Computer Games
Single-player Monte-Carlo tree search for SameGame
Knowledge-Based Systems
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This paper underlines the association of two computer go approaches, a domain-dependent knowledge approach and Monte Carlo. First, the strengthes and weaknesses of the two existing approaches are related. Then, the association is described in two steps. A first step consists in using domain-dependent knowledge within the random games enabling the program to compute evaluations that are more significant than before. A second step simply lies in pre-processing the Monte Carlo process with a knowledge-based move generator in order to speed up the program and to eliminate tactically bad moves. We set up experiments demonstrating the relevance of this association, used by Indigo at the 8th computer olympiad as well.