Titlebook: Computers and Games; 9th International Co Aske Plaat,Walter Kosters,Jaap van den Herik Conference proceedings 2016 Springer International P

inroad

书目名称Computers and Games影响因子(影响力)




书目名称Computers and Games影响因子(影响力)学科排名




书目名称Computers and Games网络公开度




书目名称Computers and Games网络公开度学科排名




书目名称Computers and Games被引频次




书目名称Computers and Games被引频次学科排名




书目名称Computers and Games年度引用




书目名称Computers and Games年度引用学科排名




书目名称Computers and Games读者反馈




书目名称Computers and Games读者反馈学科排名

STEER

Human-Side Strategies in the Werewolf Game Against the Stealth Werewolf Strategy,limitation. The solution shows that the winning rates of the human-side are more than half when the number of werewolves is assigned as in common play. Since it is thought to be fair and interesting for the winning rate to stay near 50%, the result suggests that the “stealth werewolf” strategy is no

MELD

Fast Seed-Learning Algorithms for Games,of these algorithms, namely rectangular algorithms (fully parallel) and bandit algorithms (faster in a sequential setup). We check the performance on several board games and card games. In addition, in the case of Go, we check the methodology when the opponent is completely distinct to the one used in the training.

Horizon

manifestation

https://doi.org/10.1007/978-3-642-40817-5 used not only as stand-alone players but also inside Monte Carlo Tree Search to select and bias moves. Using neural networks inside the tree search is a challenge due to their slow execution time even if accelerated on a GPU. In this paper we evaluate several strategies to limit the number of nodes

CAPE

https://doi.org/10.1007/978-3-642-40817-5ieved remarkable success in Go and other games. However, recent studies on simple regret reveal that there are better exploration strategies. To further improve the performance, a leaf to be explored is determined not only by the mean but also by the whole reward distribution. We adopted a hybrid ap

CAPE

紧张过度

FRAUD

incision

https://doi.org/10.1007/978-3-642-40817-5t computer players for 2048 uses temporal difference learning (TD learning) with .-tuple networks, and it matters a great deal how to design .-tuple networks. In this paper, we study the .-tuple networks for the game 2048. In the first set of experiments, we conduct TD learning by selecting 6- and 7