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

無(wú)關(guān)緊要

Learning to Trade in Strategic Board Gamesh 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.

browbeat

1865-0929 rth Workshop on General Intelligence in Game-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 submissi

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bacteria

Te Puna - A New Zealand Mission Stationand Realization Probability Search are shown to improve the agent considerably. Additionally, features of the static evaluation function are presented. Experimental results indicate that features, which reward distribution of the pieces and penalize pieces that clutter together, give a genuine improvement in the playing strength.

Expressly

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不規(guī)則

The Surakarta Bot Revealedand Realization Probability Search are shown to improve the agent considerably. Additionally, features of the static evaluation function are presented. Experimental results indicate that features, which reward distribution of the pieces and penalize pieces that clutter together, give a genuine improvement in the playing strength.

Interferons

Space-Consistent Game Equivalence Detection in General Game Playinge equivalence formally and concentrates on a specific scale, space-consistent game equivalence (SCGE). To detect SCGE, an approach is proposed mainly reducing the complex problem to some well-studied problems. An evaluation of the approach is performed at the end.

Charlatan

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Care of the Patient Who Misuses Drugs,of polynomial-time domain-dependent simulations, MCS is a polynomial-time algorithm as well. We observed that MCS at level 3 gives a 1.04 experimental R-approximation, which is a breakthrough. At level 1, MCS solves stacks of size 512 with an experimental R-approximation value of 1.20.