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Monte-Carlo Tree Search (MCTS) is a new best-first search guided by the results of Monte-Carlo simulations. In this article we introduce two progressive strategies for MCTS, called progressive bias and progressive unpruning. They enable the use of relatively time-expensive heuristic knowledge without speed reduction. Progressive bias directs the search(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 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)
—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) 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)
The traditional approaches to deterministic one-player games with perfect information (Kendall, Parkes, and Spoerer, 2008) are applying A* (Hart et al., 1968) or IDA* (Korf, 1985). These methods have been quite successful for solving this type of games. The disadvantage of the methods is that they require an admissible heuristic evaluation function. The(More)