Searching with probabilities
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
A Bayesian approach to relevance in game playing
Artificial Intelligence - Special issue on relevance
Partial order bounding: a new approach to evaluation in game tree search
Artificial Intelligence - Special issue on heuristic search in artificial intelligence
Artificial Intelligence - Chips challenging champions: games, computers and Artificial Intelligence
Maximizing the chance of winning in searching Go game trees
Information Sciences: an International Journal
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
Using Artificial Boundaries in the Game of Go
CG '08 Proceedings of the 6th international conference on Computers and Games
Virtual global search: application to 9×9 Go
CG'06 Proceedings of the 5th international conference on Computers and games
Hi-index | 0.00 |
Probabilistic combinatorial games (PCGs) are a model for Go-like games recently introduced by Ken Chen. They differ from normal combinatorial games since terminal positions in each subgame are evaluated by a probability distribution. The distribution expresses the uncertainty in the local evaluation. This paper focuses on the analysis and solution methods for a special case, 1-level binary PCGs. Monte-Carlo analysis is used for move ordering in an exact solver that can compute the winning probability of a PCG efficiently. Monte-Carlo interior evaluation is used in a heuristic player. Experimental results show that both types of Monte-Carlo methods work very well in this problem.