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

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Te Puna - A New Zealand Mission Stationtion five times, are revealed. The paper describes its .-based variable-depth search mechanism. Search enhancements such as multi-cut forward pruning and Realization Probability Search are shown to improve the agent considerably. Additionally, features of the static evaluation function are presented

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High Energy Neutrino Telescopes,rns in a new domain. Networks trained for a new domain are able to improve performance by selectively routing activation through previously learned neural structure, regardless of how or for what it was learned. We consider a neuroevolution implementation of the approach with application to reinforc

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Wide Angle Air Cerenkov Detectors,systems, 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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Heinrich J. V?lk,Felix A. Aharonianrm 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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https://doi.org/10.1007/978-3-319-39402-2algorithms; agents; artificial intelligence; computer games; game tree search; linear programming; machine