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We present three parallel algorithms for UCT. For 9×9 Go, they all improve the results of the programs that use them against GNU GO 3.6. The simplest one, the single-run algorithm, uses very few communications and shows improvements comparable to the more complex ones. Further improvements may be possible sharing more information in the multiple-runs(More)
—Monte Carlo Tree Search (MCTS) is state of the art for multiple games and problems. The base algorithm currently used for Monte Carlo Tree Search is UCT. We propose an alternative Monte Carlo Tree Search algorithm: Sequential Halving applied to Trees (SHOT). It has multiple advantages over UCT: it spends less time in the tree, it uses less memory, it is(More)
Many problems have a huge state space and no good heuristic to order moves so as to guide the search toward the best positions. Random games can be used to score positions and evaluate their interest. Random games can also be improved using random games to choose a move to try at each step of a game. Nested Monte-Carlo Search addresses the problem of(More)
We have parallelized our general game player Ary on a cluster of computers. We propose multiple par-allelization algorithms. For the sake of simplicity all our algorithms have processes that run independently and that join their results at the end of the thinking time in order to choose a move. Parallelization works very well for checkers, quite well for(More)
—Monte-Carlo tree search has recently been very successful for game playing particularly for games where the evaluation of a state is difficult to compute, such as Go or General Games. We compare Nested Monte-Carlo Search (NMC), Upper Confidence bounds for Trees (UCT-T), UCT with transposition tables (UCT+T) and a simple combination of NMC and UCT+T (MAX)(More)
We present a way to integrate search and Monte-Carlo methods in the game of Go. Our program uses search to find the status of tactical goals, builds groups, selects interesting goals, and computes statistics on the realization of tactical goals during the random games. The mean score of the random games where a selected tactical goal has been reached and(More)
from Poland helped improve WOLVE and MOHEX. PANORAMEX — named after Panoramix, the druid character from the Asterix and Obelix comic strip — uses the RAVE UCT formula (Gelly and Silver, 2007) with UCB exploration constant 0 and the save-bridge pattern in simulations. PANORAMEX ran on an 18-node cluster of 4-core machines, using root parallelization and(More)
Knowledge about forced moves enables to select a small number of moves from the set of possible moves. It is very important in complex domains where search trees have a large branching factor. Knowing forced moves drastically cuts the search trees. We propose a language and a metaprogram to create automatically the knowledge about interesting and forced(More)