Expected-Outcome: A General Model of Static Evaluation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computer Go: an AI oriented survey
Artificial Intelligence
Games, computers and artificial intelligence
Artificial Intelligence - Chips challenging champions: games, computers and Artificial Intelligence
Artificial Intelligence - Chips challenging champions: games, computers and 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
Associating domain-dependent knowledge and Monte Carlo approaches within a Go program
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
Efficient selectivity and backup operators in Monte-Carlo tree search
CG'06 Proceedings of the 5th international conference on Computers and games
Node-expansion operators for the UCT algorithm
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
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Progressive Pruning (PP) is employed in the Monte-Carlo Go-playing program Indigo. For each candidate move, PP launches random games starting with this move. The goal of PP is: (1) to gather statistics on moves, and (2) to prune moves statistically inferior to the best one [7]. This papers yields two new pruning techniques: Miai Pruning (MP) and Set Pruning (SP). In MP the second move of the random games is selected at random among the set of candidate moves. SP consists in gathering statistics about two sets of moves, good and bad, and it prunes the latter when statistically inferior to the former. Both enhancements clearly speed up the process of selecting a move on 9×9 boards, and MP improves slightly the playing level. Scaling up MP to 19×19 boards results in a 30% speed-up enhancement and in a four-point improvement on average.