We investigate the benefits of Tree Parallelization on a cluster for our General Game Playing program Ary. As the Tree parallelization of Monte-Carlo Tree Search works well when playouts are slow, it is of interest for General Game Playing programs, as the interpretation of game description takes a large proportion of the computing time, when compared with program designed to play specific games. We show that the tree parallelization does provide an advantage, but that it decreases for common games as the number of subplayers grows beyond 10.ou
grantor: University of TorontoThe alpha-beta algorithm is a well known method for the sequ...
University of Minnesota Ph.D. dissertation. May 2016. Major: Computer Science. Advisor: Maria Gini. ...
Monte-Carlo Tree Search (MCTS) is remarkably successful in two-player games, but parallelizing MCTS ...
We investigate the benefits of Tree Parallelization on a clus-ter for our General Game Playing progr...
We have parallelized our general game player Ary on a cluster of computers.We propose multiple paral...
International audienceMonte-Carlo Tree Search is now a well established algorithm, in games and beyo...
We present a new parallel game-tree search algorithm. Our approach classifies a processor�s availab...
Abstract. We present a game engine for general game playing based on UCT, a combination of Monte-Car...
Monte-Carlo Tree Search (MCTS) is a new best-first search method that started a revolution in the fi...
The main objective of this graduation thesis is the parallelization of the program, the core of whic...
Abstract. Monte-Carlo tree search is a powerful paradigm for the game of Go. We present a parallel M...
Abstract. Monte-Carlo tree search is a powerful paradigm for the game of Go. We present a parallel M...
The Monte-Carlo tree search (MCTS) is a method designed to solve difficult learning problems. MCTS p...
During the World Computer Chess Championships in Madrid, November 1992, our distributed chess progra...
We address the parallelization of a Monte-Carlo search algorithm. On a cluster of 64 cores we obtain...
grantor: University of TorontoThe alpha-beta algorithm is a well known method for the sequ...
University of Minnesota Ph.D. dissertation. May 2016. Major: Computer Science. Advisor: Maria Gini. ...
Monte-Carlo Tree Search (MCTS) is remarkably successful in two-player games, but parallelizing MCTS ...
We investigate the benefits of Tree Parallelization on a clus-ter for our General Game Playing progr...
We have parallelized our general game player Ary on a cluster of computers.We propose multiple paral...
International audienceMonte-Carlo Tree Search is now a well established algorithm, in games and beyo...
We present a new parallel game-tree search algorithm. Our approach classifies a processor�s availab...
Abstract. We present a game engine for general game playing based on UCT, a combination of Monte-Car...
Monte-Carlo Tree Search (MCTS) is a new best-first search method that started a revolution in the fi...
The main objective of this graduation thesis is the parallelization of the program, the core of whic...
Abstract. Monte-Carlo tree search is a powerful paradigm for the game of Go. We present a parallel M...
Abstract. Monte-Carlo tree search is a powerful paradigm for the game of Go. We present a parallel M...
The Monte-Carlo tree search (MCTS) is a method designed to solve difficult learning problems. MCTS p...
During the World Computer Chess Championships in Madrid, November 1992, our distributed chess progra...
We address the parallelization of a Monte-Carlo search algorithm. On a cluster of 64 cores we obtain...
grantor: University of TorontoThe alpha-beta algorithm is a well known method for the sequ...
University of Minnesota Ph.D. dissertation. May 2016. Major: Computer Science. Advisor: Maria Gini. ...
Monte-Carlo Tree Search (MCTS) is remarkably successful in two-player games, but parallelizing MCTS ...