Modelling adaptive systems using plausible petri nets

J. Chiachio, M. Chiachio, D. Prescott, J. Andrews

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

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Abstract

One of the main challenges when analyzing and modelling complex systems using Petri nets is to deal with uncertain information, and moreover, to be able to use such uncertainty to dynamically adapt the modelled system to uncertain (changing) contextual conditions. Such self-adaptation relies on some form of learning capability of the Petri net, which can be hardly implemented using the existing Petri net formalisms. This paper shows how uncertainty management and self-adaptation can be achieved naturally using Plausible Petri Nets, a new Petri net paradigm recently developed by the authors. The methodology is exemplified using a case study about railway track asset management, where several track maintenance and inspection activities are modelled jointly with a stochastic track geometry degradation process using a Plausible Petri net. The resulting expert system is shown to be able to autonomously adapt to contextual changes coming from noisy condition monitoring data. This adaptation is carried out taking advantage of a Bayesian updating mechanism which is inherently implemented in the execution semantics of the Plausible Petri net.
Original languageEnglish
Title of host publicationREC2018 Papers
EditorsMarco De Angelis
Place of PublicationLiverpool
Pages103-109
Number of pages7
Publication statusPublished - 18 Jul 2018
Event8th International Workshop on Reliable Computing: “Computing with Confidence” - Institute for Risk and Uncertainty, University of Liverpool, Liverpool, United Kingdom
Duration: 16 Jul 201818 Jul 2018
Conference number: 8th
http://rec2018.uk/
https://riskinstitute.uk/events/rec2018/

Conference

Conference8th International Workshop on Reliable Computing
Abbreviated titleREC2018
CountryUnited Kingdom
CityLiverpool
Period16/07/1818/07/18
Internet address

Keywords

  • uncertain information
  • Bayesian learning
  • plausible petri nets
  • infrastructure asset management

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