The SUPREM architecture: a new intelligent paradigm
Artificial Intelligence
Principles of artificial intelligence
Principles of artificial intelligence
Control strategies for two-player games
ACM Computing Surveys (CSUR)
Chess and Computers
Solving Inexact Search Problems
Solving Inexact Search Problems
Bidding algorithms for simultaneous auctions
Proceedings of the 3rd ACM conference on Electronic Commerce
World-championship-caliber Scrabble
Artificial Intelligence - Chips challenging champions: games, computers and Artificial Intelligence
Bidding Algorithms for Simultaneous Auctions: A Case Study
Autonomous Agents and Multi-Agent Systems
Monte-Carlo Tree Search Solver
CG '08 Proceedings of the 6th international conference on Computers and Games
Frequency Distribution of Contextual Patterns in the Game of Go
CG '08 Proceedings of the 6th international conference on Computers and Games
Learning to play Go using recursive neural networks
Neural Networks
Fitness Diversity Parallel Evolution Algorithms in the Turtle Race Game
EvoWorkshops '09 Proceedings of the EvoWorkshops 2009 on Applications of Evolutionary Computing: EvoCOMNET, EvoENVIRONMENT, EvoFIN, EvoGAMES, EvoHOT, EvoIASP, EvoINTERACTION, EvoMUSART, EvoNUM, EvoSTOC, EvoTRANSLOG
Monte Carlo go has a way to go
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Associating domain-dependent knowledge and Monte Carlo approaches within a Go program
Information Sciences: an International Journal
Virtual global search: application to 9×9 Go
CG'06 Proceedings of the 5th international conference on Computers and games
Efficient selectivity and backup operators in Monte-Carlo tree search
CG'06 Proceedings of the 5th international conference on Computers and games
An analysis of error recovery and sensory integration for dynamic planners
AAAI'91 Proceedings of the ninth National conference on Artificial intelligence - Volume 2
Monte-Carlo simulation balancing in practice
CG'10 Proceedings of the 7th international conference on Computers and games
Monte-Carlo opening books for amazons
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
The grand challenge of computer Go: Monte Carlo tree search and extensions
Communications of the ACM
Evaluation function based monte-carlo LOA
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
Solving probabilistic combinatorial games
ACG'05 Proceedings of the 11th international conference on Advances in Computer Games
Associating shallow and selective global tree search with monte carlo for 9 × 9 go
CG'04 Proceedings of the 4th international conference on Computers and Games
Genetic fuzzy markup language for game of NoGo
Knowledge-Based Systems
Remarks on history and presence of game tree search and research
Information Theory, Combinatorics, and Search Theory
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The expected-outcome model, in which the proper evaluation of a game-tree node is the expected value of the game's outcome given random play from that node on, is proposed. Expected outcome is considered in its ideal form, where it is shown to be a powerful heuristic. The ability of a simple random sampler that estimates expected outcome to outduel a standard Othello evaluator is demonstrated. The sampler is combined with a linear regression procedure to produce efficient expected-outcome estimators. Overall, the expected-outcome model of two-player games is shown to be precise, accurate, easily estimable, efficiently calculable, and domain-independent.