Updating Probabilistic Knowledge on Condition/Event Nets using Bayesian Networks
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.
Petri nets
Bayesian networks
Probabilistic databases
Condition/Event nets
Probabilistic knowledge
Dynamic probability distributions
Mathematics of computing~Bayesian networks
Software and its engineering~Petri nets
27:1-27:17
Regular Paper
Research partially supported by the Deutsche Forschungsgemeinschaft (DFG) under grant No. GRK 2167, Research Training Group "User-Centred Social Media".
https://arxiv.org/abs/1807.02566
Benjamin
Cabrera
Benjamin Cabrera
University of Duisburg-Essen, Germany
Tobias
Heindel
Tobias Heindel
University of Hawaii, USA
Reiko
Heckel
Reiko Heckel
University of Leicester, UK
Barbara
König
Barbara König
University of Duisburg-Essen, Germany
10.4230/LIPIcs.CONCUR.2018.27
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Benjamin Cabrera, Tobias Heindel, Reiko Heckel, and Barbara König
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