VOI感知MCTS

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摘要翻译：
UCT算法是一种最先进的蒙特卡罗树搜索算法，它是基于UCB1的多臂强盗问题(MAB)的抽样策略，该策略使累积遗憾最小。然而，搜索不同于MAB，在MCTS中，通常只有最后的“手臂拉”（实际的移动选择）才会收集奖励，而不是所有的“手臂拉”。本文提出了一种基于滚动信息值(VOI)估计的MCTS抽样策略。该政策的经验评估和UCB1和UCT的比较是在随机MAB实例以及计算机GO上进行的。
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英文标题：
《VOI-aware MCTS》
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作者：
David Tolpin and Solomon Eyal Shimony
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最新提交年份：
2012
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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一级分类：Computer Science        计算机科学
二级分类：Machine Learning        机器学习
分类描述：Papers on all aspects of machine learning research (supervised, unsupervised, reinforcement learning, bandit problems, and so on) including also robustness, explanation, fairness, and methodology. cs.LG is also an appropriate primary category for applications of machine learning methods.
关于机器学习研究的所有方面的论文（有监督的，无监督的，强化学习，强盗问题，等等），包括健壮性，解释性，公平性和方法论。对于机器学习方法的应用，CS.LG也是一个合适的主要类别。
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英文摘要：
  UCT, a state-of-the art algorithm for Monte Carlo tree search (MCTS) in games and Markov decision processes, is based on UCB1, a sampling policy for the Multi-armed Bandit problem (MAB) that minimizes the cumulative regret. However, search differs from MAB in that in MCTS it is usually only the final "arm pull" (the actual move selection) that collects a reward, rather than all "arm pulls". In this paper, an MCTS sampling policy based on Value of Information (VOI) estimates of rollouts is suggested. Empirical evaluation of the policy and comparison to UCB1 and UCT is performed on random MAB instances as well as on Computer Go.
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PDF链接：
https://arxiv.org/pdf/1207.5589

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关键词：Intelligence Presentation Applications uncertainty Presentatio 提出 搜索算法 search 移动 VOI