Abstract
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.
Highlights
Plausible reasoning is a fundamental human capability that involves the manipulation of perceptions, signs, and information from uncertain surroundings, which allows us to render an uncertain knowledge representation of reality
A novel hybrid approach for PNs has been proposed in this paper, which has been named Plausible Petri nets (PPNs), since it can effectively represent plausible information in pervasive computing environments modelled by PNs
Three illustrative examples have been provided to help the reader to conceptualise the PPN procedure, and an application example has been used to demonstrate some of the challenges faced in a real-world application of PPNs
Summary
Plausible reasoning is a fundamental human capability that involves the manipulation of perceptions, signs, and information from uncertain surroundings, which allows us to render an uncertain knowledge representation of reality. None of the PN variants developed to handle uncertainty are well-suited to embedding plausible reasoning into the PN formalism, nor do they consider the hybrid nature of real-world dynamical systems, consisting of a combination of discrete and continuous processes whose evolution may be uncertain. In this context, this paper proposes a new class of models within the PN paradigm, which has been developed by combining information theory principles with the PN technique.
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