New Results for Joint Diagnosability of Self-observed Distributed Discrete Event Systems

Lina Ye (1) , Philippe Dague (1)

1 LRI - Laboratoire de Recherche en Informatique

Diagnosability is an important property that determines at design stage how accurate any diagnosis algorithm can be on a partially observable system. Most existing approaches assumed that each observable event in the system is globally observed. Considering the cases where there is no global information, a recent work has proposed a new framework to check diagnosability in a system where each component can only observe its own observable events to keep the internal structure private in terms of observations. However, the authors implicitly assume that the local paths in each component can be exhaustively enumerated, which is not true in a general case where there are embedded cycles. In this paper, we get some new results about diagnosability in such a system, i.e., what we call joint diagnosability in a self-observed distributed system. First we prove its undecidability with unobservable communication events by reducing the Post's Correspondence Problem (PCP) to an observation problem, inspired from an existing work. Then we propose an algorithm to check a sufficient but not necessary condition of joint diagnosability. Finally we briefly discuss about the decidable case where communication events are all observable.

Domaines: Intelligence artificielle [cs.AI]

Lina Ye, Philippe Dague. New Results for Joint Diagnosability of Self-observed Distributed Discrete Event Systems. DX - 23rd International Workshop on Principles of Diagnosis, Jul 2012, Great Malvern, United Kingdom. ⟨hal-00790146⟩