By Tristan Cazenave, Mark H.M. Winands, Hiroyuki Iida (eds.)

This e-book constitutes the refereed complaints of the pc video games Workshop, CGW 2013, held in Beijing, China, in August 2013, together with the Twenty-third foreign convention on synthetic Intelligence, IJCAI 2013. The nine revised complete papers provided have been rigorously reviewed and chosen from 15 submissions. The papers hide a variety of issues regarding machine video games. They speak about six video games which are performed by means of people in perform: Chess, Domineering, chinese language Checkers, move, Goofspiel, and Tzaar. furthermore, there are papers concerning the Sliding Tile Puzzle, an software, particularly, Cooperative Path-Finding difficulties, and on basic video game playing.

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Additional info for Computer Games: Workshop on Computer Games, CGW 2013, Held in Conjunction with the 23rd International Conference on Artificial Intelligence, IJCAI 2013, Beijing, China, August 3, 2013, Revised Selected Papers

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4 and we add an analysis of convergence to NE. Finnsson applied simultaneous move MCTS to several games, including small games of Goofspiel [12, Chapter 6]. This work focused mainly on pruning provably dominated moves. Their algorithm uses solutions to linear programs in the framework of Score-Bounded MCTS [6] to extend the ideas of MCTS-Solver [27] to simultaneous move games. Saffidine et al. [23] and Bosansky et al. [3] recently described methods for αβ pruning in simultaneous move games, and also applied their algorithms to simplified Goofspiel.

This causes certain functionally equivalent patterns to become equal. Due to computational concerns, their practical use in Computer Go has been limited — one notable concept arising from this representation is the CFG distance [2]. The decision tree approach in this work makes use of another graph representation for Go positions. 5 Decision Trees Decision trees are tree structures with queries at internal nodes and values at leaves [12]. The queries evaluate the attributes of an input data point.

For each g, first each node scans its part of the array and generates the successors as described in Algorithm 1. But instead of directly updating the PDB, each node collects the successors locally. In the shuffle phase (Fig. 2), these successors are sent to the nodes storing the corresponding partitions in the PDB. Finally, the PDB is updated locally. Dealing with Memory Limitations. The parallel implementation requires more memory than the sequential algorithm, because successors are cached locally before they are stored in the array.

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