Titlebook: Advances in Computer Games; 14th International C Aske Plaat,Jaap van den Herik,Walter Kosters Conference proceedings 2015 Springer Internat

Perigee

混合,搀杂

Reducing the Seesaw Effect with Deep Proof-Number Search,absorption spectra (or ..-edges) of the Rare-Earths. The experiments on the ..-edges in the sixties [3] were, for some early Rare-Earths, partially explained with an atomic multiplet approach, developed in the early seventies [4]. This approach was improved and generalized to all Rare-Earths by Thol

Gleason-score

Feature Strength and Parallelization of Sibling Conspiracy Number Search,orations operating in a synergistic economy work best when they leverage cultural diversity and bring the greatest range of products to the largest groups of people at the lowest possible cost. To accomplish this, individuals must endeavour to do two things simultaneously:

波动

Parameter-Free Tree Style Pipeline in Asynchronous Parallel Game-Tree Search,hts, and emotions, and the ‘arousal’ or level of consciousness. An impaired level of consciousness ranges from a slight difficulty to sustain attention to a deep coma. Assessing an impaired level of consciousness relies on observation of spontaneous behaviour and responses to speech, touch, and pain

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流逝

Galactogogue

Erratum to: Das persönliche Eherechteckers require a mini-max approach while Go and Havannah require MCTS. However, a hybrid technique is possible that has features of both mini-max and MCTS. It works by stopping the random MCTS playouts early and using an evaluation function to determine the winner of the playout. We call this algori

Aggregate

Erratum to: Das persönliche Eherechtimprove MCTS for GGP. We test the resulting algorithm named Playout Policy Adaptation (PPA) on ., ., ., ., ., ., ., ., . and .. For most of these games, PPA is better than UCT with a uniform random playout policy, with the notable exceptions of Go and Nogo.

indigenous

CEDE

Peter Apathy,Peter Bydlinski,Andreas Riedlerle success. This study proposes a family of LinUCT algorithms that incorporate LinUCB into MCTS algorithms. LinUCB is a recently developed method that generalizes past episodes by ridge regression with feature vectors and rewards. LinUCB outperforms UCB1 in contextual multi-armed bandit problems. We