Abstract
AbstractThis article provides a computational framework to model self‐adaptive expert systems using the Petri net (PN) formalism. Self‐adaptive expert systems are understood here as expert systems with the ability to autonomously learn from external inputs, like monitoring data. To this end, the Bayesian learning principles are investigated and also combined with the Plausible PNs (PPNs) methodology. PPNs are a variant within the PN paradigm, which are efficient to jointly consider the dynamics of discrete events, like maintenance actions, together with multiple sources of uncertain information about a state variable. The manuscript shows the mathematical conditions and computational procedure where the Bayesian updating becomes a particular case of a more general basic operation within the PPN execution semantics, which enables the uncertain knowledge being updated from monitoring data. The approach is general, but here it is demonstrated in a novel computational model acting as expert system for railway track inspection management taken as a case study using published data from a laboratory simulation of train loading on ballast. The results reveal self‐adaptability and uncertainty management as key enabling aspects to optimize inspection actions in railway track, only being adaptively and autonomously triggered based on the actual learnt state of track and other contextual issues, like resource availability, as opposed to scheduled periodic maintenance activities.
Highlights
Self-adaptability is an important intrinsic property that is displayed by many natural systems to deal with the challenges presented by changing environments
The simulation of the Plausible Petri nets (PNs) (PPNs)-based expert system shown in Figure 8 yields predicted information about the state variable xk, along with the sequence of discrete events, such as activation of inspection, data arrival, etc
The system identifies that no more inspections are needed. Observe that these results reveal that the PPN autonomously responds to the arrival of data through adaptation so that the sequence of discrete states is altered in response to the most up-to-date information from data Y
Summary
Self-adaptability is an important intrinsic property that is displayed by many natural systems to deal with the challenges presented by changing environments. The need to incorporate self-adaptation has been acknowledged as important in allowing engineered systems to modify their behavior in response to changing conditions with little or no human input, increasing efficiency, safety, and availability while minimizing the possibility of human errors (Krupitzer, Roth, VanSyckel, Schiele, & Becker, 2015). Through self-adaptation, this knowledge is updated to dynamically accommodate environmental and contextual changes, increasing the system efficiency and making it more resilient to the new conditions, which is one of the key aspects of intelligent systems. Numerous knowledge-based models are available to represent expert systems (Adeli & Balasubramanyam, 1988; Paek & Adeli, 1990).
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