Diagnosis from Scenarios
INRIA Rennes, France.
Diagnosis of a system consists in providing explanations to a supervisor when a fault occurs from a partial observation of the system and a model of possible executions. This paper proposes a partial order diagnosis algorithm that recovers sets of scenarios which correspond to a given observation up to unobservable events. The main difficulty is that only a subset of actions is observable, while unobservable events may still induce some causal ordering among observed events. First an offline centralized version of diagnosis is given. Then, we discuss a distribution and an online version of the algorithm.
This is joint work with B. Genest and Th. Gazagnaire.