Foundations of Information and Knowledge Systems - 9th International Symposium, FoIKS 2016, Linz, Austria, March 7-11, 2016. Proceedings
Foundations of Information and Knowledge Systems - 9th International Symposium, FoIKS 2016, Linz, Austria, March 7-11, 2016. Proceedings
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信息和知识系统基础 - 第九届国际研讨会，FoIKS 2016，奥地利林茨，2016 年 3 月 7-11 日。会议记录
DOI：
10.1007/978-3-319-30024-5_2
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发表时间：
2016
期刊：
影响因子：
--
通讯作者：
Bauters K
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文献类型：
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作者：
Bauters K
智能体在不确定环境中做出快速、理性决策的能力对于其在现实环境中的适用性至关重要。马尔可夫决策过程（MDP）提供了这样一个框架，但只能对可以表示为概率的不确定性进行建模。MDP的可能性对应物允许对不精确的信念进行建模，但它们不能准确地表示不确定性的概率来源，并且它们缺乏在概率MDP社区中找到的有效在线求解器。在本文中，我们提出了三个重要方面的艺术状态。首先，我们提出了第一个在线规划器的可能性MDP适应蒙特-卡罗树搜索（MCTS）算法。一个关键的组成部分是开发有效的搜索结构，以基于Dubois，Prade和Yager介绍的DPY变换对可能性分布进行采样。其次，我们引入了一个混合MDP模型，使我们能够表达可能性和概率的不确定性，其中的混合模型是一个适当的扩展概率和可能性MDP。第三，我们证明了MCTS算法可以很容易地应用于解决这种混合模型。
The ability of an agent to make quick, rational decisions in an uncertain environment is paramount for its applicability in realistic settings. Markov Decision Processes (MDP) provide such a framework, but can only model uncertainty that can be expressed as probabilities. Possibilistic counterparts of MDPs allow to model imprecise beliefs, yet they cannot accurately represent probabilistic sources of uncertainty and they lack the efficient online solvers found in the probabilistic MDP community. In this paper we advance the state of the art in three important ways. Firstly, we propose the first online planner for possibilistic MDP by adapting the Monte-Carlo Tree Search (MCTS) algorithm. A key component is the development of efficient search structures to sample possibility distributions based on the DPY transformation as introduced by Dubois, Prade, and Yager. Secondly, we introduce a hybrid MDP model that allows us to express both possibilistic and probabilistic uncertainty, where the hybrid model is a proper extension of both probabilistic and possibilistic MDPs. Thirdly, we demonstrate that MCTS algorithms can readily be applied to solve such hybrid models.