arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 1202.4140cs.GT
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概率不确定对策与部分观察对策的等价性

Equivalence of Games with Probabilistic Uncertainty and Partial-observation Games

Krishnendu Chatterjee, Martin Chmelik, Rupak Majumdar

摘要:

我们引入游戏的概率不确定性,一个自然的控制器合成模型,其中控制器观察系统的状态,通过不精确的传感器,提供正确的信息,当前状态与一个固定的概率。也就是说,在每个步骤中,传感器返回观测状态,并且给定观测状态,在实际当前状态上存在概率分布(由于估计误差)。控制器必须根据观察到的状态(而不是它不知道的实际当前状态)做出决定。另一方面,我们假设环境可以完美地观察当前状态。我们表明,我们的模型可以减少在多项式时间标准的部分观察随机游戏,反之亦然。因此,我们建立了精确的可判定性边界的新一类游戏,并为大多数可判定的问题建立最佳的复杂性结果。

英文摘要:

We introduce games with probabilistic uncertainty, a natural model for controller synthesis in which the controller observes the state of the system through imprecise sensors that provide correct information about the current state with a fixed probability. That is, in each step, the sensors return an observed state, and given the observed state, there is a probability distribution (due to the estimation error) over the actual current state. The controller must base its decision on the observed state (rather than the actual current state, which it does not know). On the other hand, we assume that the environment can perfectly observe the current state. We show that our model can be reduced in polynomial time to standard partial-observation stochastic games, and vice-versa. As a consequence we establish the precise decidability frontier for the new class of games, and for most of the decidable problems establish optimal complexity results.

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。

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