A new paradigm for uncertain knowledge representation by Plausible Petri nets

Manuel Chiachío, Juan Chiachío, Darren Prescott, John Andrews

Research output: Contribution to journalArticlepeer-review

17 Citations (Scopus)
12 Downloads (Pure)


This paper presents a new model for Petri nets (PNs) which combines PN principles with the foundations of information theory for uncertain knowledge representation. The resulting framework has been named Plausible Petri nets (PPNs). The main feature of PPNs resides in their efficiency to jointly consider the evolution of a discrete event system together with uncertain information about the system state using states of information. The paper overviews relevant concepts of information theory and uncertainty representation, and presents an algebraic method to formally consider the evolution of uncertain state variables within the PN dynamics. To illustrate some of the real-world challenges relating to uncertainty that can be handled using a PPN, an example of an expert system is provided, demonstrating how condition monitoring data and expert opinion can be modelled.
Original languageEnglish
Pages (from-to)323-345
Number of pages23
JournalInformation Sciences
Early online date10 Apr 2018
Publication statusPublished - 31 Jul 2018


  • Petri nets
  • information theory
  • knowledge re
  • expert systems


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  • Extraordinary PhD Award

    Chiachio-Ruano, Juan (Recipient), Nov 2018

    Prize: Prize (including medals and awards)

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