多智能体假设检验中测量的顺序效应

Aneesh Raghavan, J. Baras
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引用次数: 0

摘要

在多智能体系统中,一个智能体的事件集中的所有命题可能无法同时验证。本文回顾了文献中\textit{事件-状态-操作结构}和\textit{不相容关系}的概念,并将其作为研究事件集代数结构的工具。我们给出了一个多智能体假设检验的例子,其中事件集不形成布尔代数,而是形成一个正正交。讨论了考虑不\textit{相容事件(不能同时验证的事件})的“非交换概率空间”的可能构造。作为这种概率空间中的一个可能的决策问题,我们考虑二元假设检验问题。我们提出了两种方法来解决这个决策问题。在第一种方法中,我们将可用数据表示为来自通过投影值测量(PVM)建模的测量,并检索使用经典概率模型解决的底层检测问题的结果。在第二种方法中,我们使用正算子值度量(POVM)表示度量。我们证明了第二种方法的最小误差概率与第一种方法相同。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Order Effects of Measurements in Multi-Agent Hypothesis Testing
All propositions from the set of events for an agent in a multi-agent system might not be simultaneously verifiable. In this paper, we revisit the concepts of \textit{event-state-operation structure} and \textit{relationship of incompatibility} from literature and use them as a tool to study the algebraic structure of the set of events. We present an example from multi-agent hypothesis testing where the set of events does not form a Boolean algebra but forms an ortholattice. A possible construction of a 'noncommutative probability space', accounting for \textit{incompatible events} (events which cannot be simultaneously verified) is discussed. As a possible decision-making problem in such a probability space, we consider the binary hypothesis testing problem. We present two approaches to this decision-making problem. In the first approach, we represent the available data as coming from measurements modeled via projection valued measures (PVM) and retrieve the results of the underlying detection problem solved using classical probability models. In the second approach, we represent the measurements using positive operator valued measures (POVM). We prove that the minimum probability of error achieved in the second approach is the same as in the first approach.
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