ON STRUCTURAL ANALYSIS OF INTERACTING BEHAVIORAL PETRI NETS FOR DISTRIBUTED CAUSAL MODEL-BASED DIAGNOSIS

This paper deals with the problem of 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 analyzing the P-invariants of the corresponding BPN model. Once local diagnoses are obtained, agents begin to communicate to ensure that such diagnoses are consistent and recover completely the results obtained by a centralized agent having a global view about the whole system.