By Guillaume Chaslot, Christophe Fiter, Jean-Baptiste Hoock (auth.), H. Jaap van den Herik, Pieter Spronck (eds.)
This quantity constitutes the completely refereed post-conference court cases of the 12th Advances in computing device video games convention, ACG 2009, held in Pamplona, Spain, in may possibly 2009. The 20 revised complete papers offered have been rigorously reviewed and chosen from forty-one submissions for inclusion within the ebook. the subjects addressed comprise Monte-Carlo tree seek, Bayesian modeling, selective seek, brute strength, clash answer, fixing video games, optimization, idea discovery, incongruity concept, and knowledge insurance.
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Further optimization of distributing virtual wins should be possible, and is part of our plans for future work. 2). 1 Testing MCTS against JSettlers Against the baseline heuristics of JSettlers we tested three diﬀerent AIs: the random player, MCTS with N = 1000 simulated games per move, and MCTS with N = 10000. In all experiments, three JSettlers agents were playing against one Monte-Carlo Tree Search in Settlers of Catan 29 other AI. For each experiment, 100 games were played. Following our preliminary investigations, we assume that no biasing terms are present, so results for the three JSettlers agents are interchangeable.
The black pieces are placed along the top and bottom rows of the board, while the white pieces are placed in the left- and right-most ﬁles of the board (see Fig. 1a). The players alternately move a piece, starting with Black. A piece moves in a straight line, exactly as many squares as there are pieces of either color anywhere along the line of movement (see Fig. 1b). A player may jump over its own pieces, but not over the opponent’s. The rule is that opponent’s pieces are captured (and removed from the board) by landing on them.
Chaslot, and P. Spronck Probabilities of choosing an action in the Monte-Carlo simulation were proportional to their weights. The settings seem reasonable according to our experience with the game and with expert advice on Monte-Carlo simulations. However, the actual performance of our agent dropped signiﬁcantly when using the modiﬁed probabilities instead of uniform sampling. A possible explanation is that with the given weights, the agents are too eager to build settlements and cities, while there may be a strategic advantage in giving preference to extending the roads network to reach a better strategic position.