Titlebook: Computer Games; Fourth Workshop on C Tristan Cazenave,Mark H.M. Winands,Julian Togelius Conference proceedings 2016 Springer International

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The , System: Learning Board Game Rules with Piece-Move Interactionssystems, is a time-consuming and error-prone activity. In order to counter these difficulties, efforts have been made in various communities to learn the models from input data. One learning approach is to learn models from example transition sequences. Learning state transition systems from example

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Creating Action Heuristics for General Game Playing Agentsrm well in the absence of domain knowledge. Several approaches have been proposed to add heuristics to MCTS in order to guide the simulations. In GGP those approaches typically learn heuristics at runtime from the results of the simulations. Because of peculiarities of GGP, it is preferable that the

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485 – A New Upper Bound for Morpion Solitaire By solving continuous-valued relaxations of linear programs on these boards, we obtain an upper bound of 586 moves. Further analysis of original, not relaxed, mixed-integer programs leads to an improvement of this bound to 485 moves. However, this is achieved at a significantly higher computational cost.

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On the Cross-Domain Reusability of Neural Modules for General Video Game Playingement learning domains. This approach is more general than previous approaches to transfer for reinforcement learning. It is domain-agnostic and requires no prior assumptions about the nature of task relatedness or mappings. We analyze the method’s performance and applicability in high-dimensional Atari 2600 general video game playing.

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Conference proceedings 2016e-Playing Agents, GIGA 2015, held in conjunction with the 24th International Conference on Artificial Intelligence, IJCAI 2015, Buenos Aires, Argentina, in July 2015..The 12 revised full papers presented were carefully reviewed and selected from 27 submissions. The papers address all aspects of arti

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Michael H. Bross,David C. Campbelllion possible positions and is stored using 500 GB of disk space. In this paper we report results from a preliminary study on how to best use the data to improve the play of a Chinese Checkers program.

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Te Puna - A New Zealand Mission Stationh in an offline setting and online while playing the game against a rule-based baseline. Experimental results show that agents trained from data from average human players can outperform rule-based trading behavior, and that the Random Forest model achieves the best results.

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Challenges and Progress on Using Large Lossy Endgame Databases in Chinese Checkerslion possible positions and is stored using 500 GB of disk space. In this paper we report results from a preliminary study on how to best use the data to improve the play of a Chinese Checkers program.