Modular structural analysis of Petri nets for distributed causal model-based diagnosis

This paper describes an empirical evaluation of a modular structural analysis technique to distributed causal model-based diagnosis, in case the behavioral model of the system under consideration is described through a set of place-bordered behavioral Petri nets (BPNs). In particular, each BPN model is diagnosed by a diagnostic agent on the basis of its local model, the local received observation and the information exchanged with the neighboring agents. The interactions between BPNs are captured by tokens that may pass from one net model to another via bordered places. We show that the structural analysis based on P-invariants of each net model, can improve the performance of causal model-based diagnosis of distributed systems compared to that based on reachability graphs.