文章基本信息

标题：Updating Probabilistic Knowledge on Condition/Event Nets using Bayesian Networks
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作者：Benjamin Cabrera ; Tobias Heindel ; Reiko Heckel 等
期刊名称：LIPIcs : Leibniz International Proceedings in Informatics
电子版ISSN：1868-8969
出版年度：2018
卷号：118
页码：1-17
DOI：10.4230/LIPIcs.CONCUR.2018.27
出版社：Schloss Dagstuhl -- Leibniz-Zentrum fuer Informatik
摘要：The paper extends Bayesian networks (BNs) by a mechanism for dynamic changes to the probability distributions represented by BNs. One application scenario is the process of knowledge acquisition of an observer interacting with a system. In particular, the paper considers condition/event nets where the observer's knowledge about the current marking is a probability distribution over markings. The observer can interact with the net to deduce information about the marking by requesting certain transitions to fire and observing their success or failure. Aiming for an efficient implementation of dynamic changes to probability distributions of BNs, we consider a modular form of networks that form the arrows of a free PROP with a commutative comonoid structure, also known as term graphs. The algebraic structure of such PROPs supplies us with a compositional semantics that functorially maps BNs to their underlying probability distribution and, in particular, it provides a convenient means to describe structural updates of networks.
关键词：Petri nets; Bayesian networks; Probabilistic databases; Condition/Event nets; Probabilistic knowledge; Dynamic probability distributions