Title: Distributed diagnosis based on distributed probability propagation nets

Authors: Yasser Moussa Berghout; Hammadi Bennoui

Addresses: LINFI Lab. and Department of Computer Science, University of Biskra, 07000, Biskra, Algeria ' LINFI Lab. and Department of Computer Science, University of Biskra, 07000, Biskra, Algeria

Abstract: This paper addresses the problem of modelling uncertainty in the distributed context. It is situated in the field of diagnosis; more precisely, model-based diagnosis of distributed systems. A special focus is given to modelling uncertainty and probabilistic reasoning. Thus, this work is based on a probabilistic modelling formalism called: 'probability propagation nets' (PPNs), which are designed for centralised systems. Hence, an extension of this model is proposed to suit the distributed context. Distributed probability propagation nets (DPPNs), the proposed extension, were conceived to consider the distributed systems' particularities. So, the set we consider is a set of interacting subsystems, each of which is modelled by a DPPN. The interaction among the subsystems is modelled through the firing of common transitions belonging to more than one subsystem. All of that is logically supported by means of probabilistic Horn abductions (PHAs). Furthermore, the diagnostic process is done by exploiting transition-invariants, a diagnostic technique developed for Petri nets. The proposed extension is illustrated through a real life example.

Keywords: model-based diagnosis; distributed systems; probabilistic reasoning; probability propagation nets; PPNs; probabilistic Horn abduction; PHA; Petri nets; transition invariants; causal models.

DOI: 10.1504/IJCSE.2019.10017857

International Journal of Computational Science and Engineering, 2019 Vol.18 No.1, pp.72 - 79

Received: 28 Dec 2015
Accepted: 24 Jul 2016

Published online: 14 Dec 2018 *

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