Expected-Outcome: A General Model of Static Evaluation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Finite-time Analysis of the Multiarmed Bandit Problem
Machine Learning
Optimal unbiased estimators for evaluating agent performance
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Simulation-based approach to general game playing
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 1
Monte-Carlo exploration for deterministic planning
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
A study on security evaluation methodology for image-based biometrics authentication systems
BTAS'09 Proceedings of the 3rd IEEE international conference on Biometrics: Theory, applications and systems
Efficient selectivity and backup operators in Monte-Carlo tree search
CG'06 Proceedings of the 5th international conference on Computers and games
The true score of statistical paraphrase generation
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Scalability and parallelization of Monte-Carlo tree search
CG'10 Proceedings of the 7th 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
Bandit based monte-carlo planning
ECML'06 Proceedings of the 17th European conference on Machine Learning
Design and parametric considerations for artificial neural network pruning in UCT game playing
Proceedings of the South African Institute for Computer Scientists and Information Technologists Conference
Scalable and efficient bayes-adaptive reinforcement learning based on monte-carlo tree search
Journal of Artificial Intelligence Research
| Hi-index | 48.22 |
The ancient oriental game of Go has long been considered a grand challenge for artificial intelligence. For decades, computer Go has defied the classical methods in game tree search that worked so successfully for chess and checkers. However, recent play in computer Go has been transformed by a new paradigm for tree search based on Monte-Carlo methods. Programs based on Monte-Carlo tree search now play at human-master levels and are beginning to challenge top professional players. In this paper, we describe the leading algorithms for Monte-Carlo tree search and explain how they have advanced the state of the art in computer Go.