Learn More
Recently, Monte-Carlo Tree Search (MCTS) has advanced the field of computer Go substantially. In this article we investigate the application of MCTS for the game Lines of Action (LOA). A new MCTS variant, called MCTS-Solver, has been designed to play narrow tactical lines better in sudden-death games such as LOA. The variant differs from the traditional(More)
Monte-Carlo Tree Search (MCTS) is a new best-first search method that started a revolution in the field of Computer Go. Paral-lelizing MCTS is an important way to increase the strength of any Go program. In this article, we discuss three parallelization methods for MCTS: leaf parallelization, root parallelization, and tree parallelization. To be effective(More)
This paper proposes a new algorithm, called best reply search (BRS), for deterministic multiplayer games with perfect information. In BRS, only the opponent with the strongest counter move is allowed to make a move. More turns of the root player can be searched resulting in long-term planning. We test BRS in the games of Chinese Checkers, Focus, and(More)
The success of Monte Carlo tree search (MCTS) in many games, where αβ-based search has failed, naturally raises the question whether Monte Carlo simulations will eventually also outperform traditional game-tree search in game domains where αβ -based search is now successful. The forte of αβ-based search are highly(More)
This paper investigates to what extent learning methods are beneficial for the Lines of Action tournament program MIA. We focus on two components of the program: (1) the evaluation function and (2) the move ordering. Using temporal difference learning the evaluation function was improved by tuning the weights. We found substantial improvements for three(More)
Classic methods such as A* and IDA* are a popular and successful choice for one-player games. However, without an accurate admissible evaluation function , they fail. In this article we investigate whether Monte-Carlo Tree Search (MCTS) is an interesting alternative for one-player games where A* and IDA* methods do not perform well. Therefore, we propose a(More)
The aim of general game playing (GGP) is to create programs capable of playing a wide range of different games at an expert level, given only the rules of the game. The most successful GGP programs currently employ simulation-based Monte Carlo tree search (MCTS). The performance of MCTS depends heavily on the simulation strategy used. In this paper, we(More)
Monte-Carlo Tree Search (MCTS) is a recent paradigm for game-tree search, which gradually builds a game-tree in a best-first fashion based on the results of randomized simulation play-outs. The performance of such an approach is highly dependent on both the total number of simulation play-outs and their quality. The two metrics are, however, typically(More)
Monte Carlo Tree Search (MCTS) has become a widely popular sampled-based search algorithm for two-player games with perfect information. When actions are chosen simultaneously, players may need to mix between their strategies. In this paper, we discuss the adaptation of MCTS to simultaneous move games. We introduce a new algorithm, Online Outcome Sampling(More)