生命是随机的，时间不是：具有窗口目标的马尔可夫决策过程
Life is Random, Time is Not: Markov Decision Processes with Window Objectives
AI总结：
本研究将窗口目标扩展到马尔可夫决策过程，解决阈值概率问题，提出通用方法并应用于均值支付和对偶目标，为随机模型中的窗口机制应用奠定基础。
摘要：
窗口机制由Chatterjee等人提出，旨在通过时间界限强化经典博弈目标。与仅要求行为在极限情况下正确的传统目标不同，窗口机制允许综合系统控制器，使其在无限执行过程中，在可配置的时间框架内表现出可接受的行为。窗口概念已在多种双人零和博弈中显示出其价值，因为它能够在系统规范中推理此类时间界限，并且通常能提高可处理性。\n 在本工作中，我们将窗口框架扩展到随机环境，考虑马尔可夫决策过程。此背景下的一个基本问题是阈值概率问题：给定一个目标，旨在综合策略以保证以给定概率满足运行。我们针对窗口目标的常见变体解决了该问题，其中时间框架要么作为参数设定，要么询问是否存在这样的时间框架。我们为基于窗口的目标开发了一种通用方法，并将其应用于经典的均值支付和对偶目标，这些目标已在博弈中考虑过。我们的工作为窗口机制在随机模型中的广泛使用铺平了道路。
英文摘要：
The window mechanism was introduced by Chatterjee et al. to strengthen classical game objectives with time bounds. It permits to synthesize system controllers that exhibit acceptable behaviors within a configurable time frame, all along their infinite execution, in contrast to the traditional objectives that only require correctness of behaviors in the limit. The window concept has proved its interest in a variety of two-player zero-sum games because it enables reasoning about such time bounds in system specifications, but also thanks to the increased tractability that it usually yields. In this work, we extend the window framework to stochastic environments by considering Markov decision processes. A fundamental problem in this context is the threshold probability problem: given an objective it aims to synthesize strategies that guarantee satisfying runs with a given probability. We solve it for the usual variants of window objectives, where either the time frame is set as a parameter, or we ask if such a time frame exists. We develop a generic approach for window-based objectives and instantiate it for the classical mean-payoff and parity objectives, already considered in games. Our work paves the way to a wide use of the window mechanism in stochastic models.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
