在动态贝叶斯网络中利用因果关系进行选择性信念过滤（扩展摘要）
Exploiting Causality for Selective Belief Filtering in Dynamic Bayesian Networks (Extended Abstract)
摘要：
动态贝叶斯网络（DBNs）是一种研究状态部分可观测随机过程的通用模型。DBN中的信念过滤是基于不完整和不确定的观察来推断信念状态（即过程状态的概率分布）的任务。在这篇文章中，我们探索了通过自动利用过程中的因果关系来加速过滤任务的想法。我们考虑一种特定类型的因果关系，称为被动性，它涉及状态变量如何引起其他变量的变化。我们提出了基于被动性的选择性信念过滤（PSBF）方法，该方法保持了一个因子化的信念表示，并利用被动性对信念因子进行选择性更新。PSBF在合成过程和模拟的多机器人仓库，在那里它优于其他过滤方法，利用被动。
英文摘要：
Dynamic Bayesian networks (DBNs) are a general model for stochastic processes with partially observed states. Belief filtering in DBNs is the task of inferring the belief state (i.e. the probability distribution over process states) based on incomplete and uncertain observations. In this article, we explore the idea of accelerating the filtering task by automatically exploiting causality in the process. We consider a specific type of causal relation, called passivity, which pertains to how state variables cause changes in other variables. We present the Passivity-based Selective Belief Filtering (PSBF) method, which maintains a factored belief representation and exploits passivity to perform selective updates over the belief factors. PSBF is evaluated in both synthetic processes and a simulated multi-robot warehouse, where it outperformed alternative filtering methods by exploiting passivity.
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。
