Modelling adaptive systems using plausible Petri nets

Chiach�o, J.; Chiach�o, M.; Prescott, D.; Andrews, J.

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 [Information Sciences , 453 (2018) pp. 323-345]. 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.

Citation

Chiachío, J., Chiachío, M., Prescott, D., & Andrews, J. (2018). Modelling adaptive systems using plausible Petri nets. In 8th International Workshop on Reliable Engineering Computing, “ Computing with Confidence ”

• Conference Name: 8th International Workshop on Reliable Engineering Computing, “ Computing with Confidence ”
• Start Date: Jul 16, 2018
• End Date: Jul 18, 2018
• Acceptance Date: May 30, 2018
• Online Publication Date: Jul 18, 2018
• Publication Date: Jul 18, 2018
• Deposit Date: Sep 19, 2018
• Publicly Available Date: Sep 19, 2018
• Book Title: 8th International Workshop on Reliable Engineering Computing, “ Computing with Confidence ”
• Chapter Number: 15
• ISBN: N/A
• Keywords: Uncertain information, Bayesian learning, Plausible Petri nets, Infrastructure Asset Management
• Public URL: https://nottingham-repository.worktribe.com/output/1090589
• Related Public URLs: http://riskinstitute.org.uk/rec2018/index.php?uri=papers