A new paradigm for uncertain knowledge representation by Plausible Petri nets

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 con...

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Main Authors: Chiachío, Manuel, Chiachío, Juan, Prescott, Darren, Andrews, John
Format: Article
Published: Elsevier 2018
Subjects:
Online Access:https://eprints.nottingham.ac.uk/51406/
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author Chiachío, Manuel
Chiachío, Juan
Prescott, Darren
Andrews, John
author_facet Chiachío, Manuel
Chiachío, Juan
Prescott, Darren
Andrews, John
author_sort Chiachío, Manuel
building Nottingham Research Data Repository
collection Online Access
description 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.
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institution University of Nottingham Malaysia Campus
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publishDate 2018
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spelling nottingham-514062020-05-04T19:47:35Z https://eprints.nottingham.ac.uk/51406/ A new paradigm for uncertain knowledge representation by Plausible Petri nets Chiachío, Manuel Chiachío, Juan Prescott, Darren Andrews, John 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. Elsevier 2018-07-31 Article PeerReviewed Chiachío, Manuel, Chiachío, Juan, Prescott, Darren and Andrews, John (2018) A new paradigm for uncertain knowledge representation by Plausible Petri nets. Information Sciences, 453 . pp. 323-345. ISSN 1872-6291 Petri nets; Information theory; Knowledge representation; Expert systems https://doi.org/10.1016/j.ins.2018.04.029 doi:10.1016/j.ins.2018.04.029 doi:10.1016/j.ins.2018.04.029
spellingShingle Petri nets; Information theory; Knowledge representation; Expert systems
Chiachío, Manuel
Chiachío, Juan
Prescott, Darren
Andrews, John
A new paradigm for uncertain knowledge representation by Plausible Petri nets
title A new paradigm for uncertain knowledge representation by Plausible Petri nets
title_full A new paradigm for uncertain knowledge representation by Plausible Petri nets
title_fullStr A new paradigm for uncertain knowledge representation by Plausible Petri nets
title_full_unstemmed A new paradigm for uncertain knowledge representation by Plausible Petri nets
title_short A new paradigm for uncertain knowledge representation by Plausible Petri nets
title_sort new paradigm for uncertain knowledge representation by plausible petri nets
topic Petri nets; Information theory; Knowledge representation; Expert systems
url https://eprints.nottingham.ac.uk/51406/
https://eprints.nottingham.ac.uk/51406/
https://eprints.nottingham.ac.uk/51406/