Updating Probabilistic Knowledge on Condition/Event Nets using Bayesian Networks

Authors Benjamin Cabrera, Tobias Heindel, Reiko Heckel, Barbara König

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Author Details

Benjamin Cabrera
  • University of Duisburg-Essen, Germany
Tobias Heindel
  • University of Hawaii, USA
Reiko Heckel
  • University of Leicester, UK
Barbara König
  • University of Duisburg-Essen, Germany

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Benjamin Cabrera, Tobias Heindel, Reiko Heckel, and Barbara König. Updating Probabilistic Knowledge on Condition/Event Nets using Bayesian Networks. In 29th International Conference on Concurrency Theory (CONCUR 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 118, pp. 27:1-27:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)


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.

Subject Classification

ACM Subject Classification
  • Mathematics of computing → Bayesian networks
  • Software and its engineering → Petri nets
  • Petri nets
  • Bayesian networks
  • Probabilistic databases
  • Condition/Event nets
  • Probabilistic knowledge
  • Dynamic probability distributions


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