Sequential Halving Applied to Trees

Q2 Computer Science

IEEE Transactions on Computational Intelligence and AI in Games Pub Date : 2015-03-01 DOI:10.1109/TCIAIG.2014.2317737

T. Cazenave

引用次数: 23

Abstract

Monte Carlo tree search (MCTS) is state of the art for multiple games and problems. The base algorithm currently used for MCTS is UCT. We propose an alternative MCTS algorithm: sequential halving applied to Trees (SHOT). It has multiple advantages over UCT: it spends less time in the tree, it uses less memory, it is parameter free, at equal time settings it beats UCT for a complex combinatorial game and it can be efficiently parallelized.

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适用于树的顺序减半

蒙特卡罗树搜索(MCTS)是解决多个游戏和问题的最先进的方法。目前用于MCTS的基本算法是UCT。我们提出了一种替代的MCTS算法:应用于树的顺序减半(SHOT)。它比UCT有很多优点:它在树中花费的时间更少，使用的内存更少，没有参数，在相同的时间设置下，它在复杂的组合游戏中优于UCT，并且它可以有效地并行化。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Transactions on Computational Intelligence and AI in Games COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-COMPUTER SCIENCE, SOFTWARE ENGINEERING

CiteScore

4.60

自引率

0.00%

发文量

审稿时长

>12 weeks

期刊介绍： Cessation. The IEEE Transactions on Computational Intelligence and AI in Games (T-CIAIG) publishes archival journal quality original papers in computational intelligence and related areas in artificial intelligence applied to games, including but not limited to videogames, mathematical games, human–computer interactions in games, and games involving physical objects. Emphasis is placed on the use of these methods to improve performance in and understanding of the dynamics of games, as well as gaining insight into the properties of the methods as applied to games. It also includes using games as a platform for building intelligent embedded agents for the real world. Papers connecting games to all areas of computational intelligence and traditional AI are considered.