Interacting behavioral Petri nets analysis for distributed causal model-based diagnosis

Abstract

This paper deals with the problem of spatially distributed causal model-based diagnosis on interacting behavioral petri nets (BPNs). The system to be diagnosed comprises different interacting subsystems (each modeled as a BPN) and the diagnostic system is defined as a multi-agent system where each agent is designed to diagnose a particular subsystem on the basis of its local model, the local received observation and the information exchanged with the neighboring agents. The interactions between subsystems are captured by tokens that may pass from one net model to another via bordered places. The diagnostic reasoning scheme is accomplished locally within each agent by exploiting classical analysis techniques of Petri nets like reachability graph and invariant analysis. Once local diagnoses are obtained, agents begin to communicate to ensure that such diagnoses are consistent and recover completely the results that would be obtained by a centralized agent having a global view about the whole system. The paper concludes with an empirical comparison, in terms of the running time, of two implementations of Petri net analysis techniques used as a distributed diagnostic reasoning schemes.