Distributed Causal Model-Based Diagnosis Based on Interacting Behavioral Petri Nets

Distributed diagnosis based on distributed probability propagation nets

“…After tracking the sources of a given observation using a T-invariant approach, two possible solutions emerged, ε 1 and ε 2, with different probabilities, as a basis to rank them. Remark 1 points out to the problem of a possible inconsistency between the computed diagnoses due to the difference in knowledge acquired by each diagnoser (see Bennoui et al, 2009). Hence, having a diagnoser with a global view on the system somewhat ensures the consistency of local diagnosers knowledge.…”

“…The common transitions indicate that there is a synchronisation and order among the components and operations of the subsystems. In a lot of works in the literature, such as Jiroveanu and Boel (2005), Genc and Lafortune (2007) and Bennoui et al (2009), the petri net modelling of the interaction is done through common places. In our case, we choose to model it by the common transitions approach (Baldan et al, 2005) as it is more suited for PPNs, because the canonical p/t-net representation requires starting and ending up with transitions.…”

Exploiting P-invariant analysis for distributed systems diagnosis based on interacting behavioral Petri nets

“…In order to identify such initial marking, we need to go back from the places modelling the observed manifestations to source places by firing transitions in a backward fashion. This has been the preserve of an approach (BW-analysis) presented in [3] that exploits the backward reachability graphs as a diagnostic reasoning mechanism. Thus, the major shortcoming of the mentioned approach is the state space explosion problem especially when BPNs models become large.…”

“…After receiving such a message, agent A j will respond to A i by either a positive reply or negative one; depending on the fact that at least one its local diagnoses is consistent with the received message. In order to check such a consistency, [3] proposes to construct from each of A j 's local diagnoses the forward reachability graph to see if tokens requested by A i are produced by the net model of A j through its corresponding output places when supposing such a diagnosis. Thus, besides the state space explosion during local computations, the communication protocol suffers of the same problem in refining the set of local diagnoses of different agents because it exploits several reachability graphs as a basis for consistency checking for each exchanged message.…”