A temporal logic for Markov chains
A temporal logic for Markov chains
复制标题
马尔可夫链的时态逻辑
DOI：
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发表时间：
2008
期刊：
影响因子：
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通讯作者：
W. Jamroga
中科院分区：
文献类型：
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作者：
W. Jamroga
大多数智能体和多智能体系统的模型都包含系统可能状态的信息（定义状态之间的关系及其外部特征），以及状态之间关系的信息。这类定性模型没有为这些关系分配数值度量。与此同时，定量模型假设关系是可测量的，并提供有关关系程度的数字信息。在本文中，我们探讨了一些定性和定量的代理/过程模型之间的类比，特别是那些之间的过渡系统和马尔可夫模型。
对马尔可夫过程模型的典型分析只涉及过程所能获得的期望效用。另一方面，模态逻辑通过组合各种模态算子提供了一种描述现象的系统方法。在这里，我们试图利用语言功能，提供命题模态逻辑，马尔可夫链和马尔可夫决策过程的分析。为此，我们提出马尔可夫时序逻辑-一个多值逻辑，扩展了分支时间逻辑CTL*。
Most models of agents and multi-agent systems include information about possible states of the system (that defines relations between states and their external characteristics), and information about relationships between states. Qualitative models of this kind assign no numerical measures to these relationships. At the same time, quantitative models assume that the relationships are measurable, and provide numerical information about the degrees of relations. In this paper, we explore the analogies between some qualitative and quantitative models of agents/processes, especially those between transition systems and Markovian models.
Typical analysis of Markovian models of processes refers only to the expected utility that can be obtained by the process. On the other hand, modal logic offers a systematic method of describing phenomena by combining various modal operators. Here, we try to exploit linguistic features, offered by propositional modal logic, for analysis of Markov chains and Markov decision processes. To this end, we propose Markov temporal logic - a multi-valued logic that extends the branching time logic CTL*.