The History Heuristic and Alpha-Beta Search Enhancements in Practice
IEEE Transactions on Pattern Analysis and Machine Intelligence
Expected-Outcome: A General Model of Static Evaluation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Using probabilistic knowledge and simulation to play poker
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Computer Go: an AI oriented survey
Artificial Intelligence
Artificial Intelligence - Chips challenging champions: games, computers and Artificial Intelligence
World-championship-caliber Scrabble
Artificial Intelligence - Chips challenging champions: games, computers and Artificial Intelligence
Finite-time Analysis of the Multiarmed Bandit Problem
Machine Learning
Honte, a go-playing program using neural nets
Machines that learn to play games
Combining online and offline knowledge in UCT
Proceedings of the 24th international conference on Machine learning
Sample-based learning and search with permanent and transient memories
Proceedings of the 25th international conference on Machine learning
CG '08 Proceedings of the 6th international conference on Computers and Games
An Analysis of UCT in Multi-player Games
CG '08 Proceedings of the 6th international conference on Computers and Games
Monte-Carlo simulation balancing
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Simulation-based approach to general game playing
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 1
Achieving master level play in 9×9 computer go
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
Reinforcement learning of local shape in the game of go
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
UCT for tactical assault planning in real-time strategy games
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Introduction of a new paraphrase generation tool based on Monte-Carlo sampling
ACLShort '09 Proceedings of the ACL-IJCNLP 2009 Conference Short Papers
Associating domain-dependent knowledge and Monte Carlo approaches within a Go program
Information Sciences: an International Journal
Efficient selectivity and backup operators in Monte-Carlo tree search
CG'06 Proceedings of the 5th international conference on Computers and games
Reinforcement learning and simulation-based search in computer go
Reinforcement learning and simulation-based search in computer go
Monte-Carlo simulation balancing in practice
CG'10 Proceedings of the 7th international conference on Computers and games
Bandit based monte-carlo planning
ECML'06 Proceedings of the 17th European conference on Machine Learning
Evaluation function based monte-carlo LOA
ACG'09 Proceedings of the 12th international conference on Advances in Computer Games
Creating an upper-confidence-tree program for havannah
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
The grand challenge of computer Go: Monte Carlo tree search and extensions
Communications of the ACM
Monte-Carlo tree search for the physical travelling salesman problem
EvoApplications'12 Proceedings of the 2012t European conference on Applications of Evolutionary Computation
Bitboard knowledge base system and elegant search architectures for Connect6
Knowledge-Based Systems
Procedural knowledge for integrated modelling: Towards the Modelling Playground
Environmental Modelling & Software
A survey of multi-objective sequential decision-making
Journal of Artificial Intelligence Research
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A new paradigm for search, based on Monte-Carlo simulation, has revolutionised the performance of computer Go programs. In this article we describe two extensions to the Monte-Carlo tree search algorithm, which significantly improve the effectiveness of the basic algorithm. When we applied these two extensions to the Go program MoGo, it became the first program to achieve dan (master) level in 9x9 Go. In this article we survey the Monte-Carlo revolution in computer Go, outline the key ideas that led to the success of MoGo and subsequent Go programs, and provide for the first time a comprehensive description, in theory and in practice, of this extended framework for Monte-Carlo tree search.