Publications

This paper deals with the incremental off-line computation of diagnosis of discrete-event systems. Traditionally, the diagnosis is computed from the global automaton describing the observations emitted by the system on a whole time period. The idea of this paper is to slice this global automaton according to temporal windows and to compute local diagnoses for each of these windows. It is shown that, under some conditions, the global diagnosis can be computed from the local diagnosis.
This paper presents the formalization used to compute an incremental diagnosis, relying on the new concept of {\it automata chain}. It is then shown that it is possible to take into account the diagnosis obtained for the previous temporal windows to incrementally compute the current diagnosis more efficiently. This work is a first and necessary step before considering the on-line diagnosis computation. The main difficulty is then to ensure the correct slicing of the observation automaton and to determine the appropriate temporal windows.

On-line reconfiguration is the ability to rearrange dynamically the elements of a system to accommodate failure events or new requirements. Due to the modular representation, decentralized discrete-event approach, recently proposed for the diagnosis of systems, is particularly well suited to the diagnosis of reconfigurable systems. The contribution of this article is to extend our decentralized approach to reconfigurable discrete-event systems. A first step in this direction is to extend the way a decentralized system is modelled. The idea consists in modelling separately the behavior of the components and the system topology.
A second step is to formally define what is a reconfiguration. A property of reconfiguration, that we call \ital{safety}, is identified to be important. When satisfied, we show that our decentralized diagnosis approach can easily be extended to reconfigurable systems.

Le temps de calcul des trajectoires sur un modèle de comportement du système est un problème critique rencontré aussi bien en diagnostic qu'en planification. Dans le but d'améliorer l'efficacité de cette tâche, un intérêt croissant est porté aux techniques de model-checking développées dans le domaine de la vérification automatique. Dans cet article, nous proposons de représenter le système par un automate, et nous définissons une nouvelle propriété appelée interversibilité. Cette propriété est utilisée pour améliorer l'efficacité de l'algorithme de recherche calculant les trajectoires. Nous présentons deux exemples dans les domaines du diagnostic et de la planification où cette approche donne des résultats satisfaisants.

A time-consuming problem encountered both in system diagnosis and planning is that of computing trajectories over a behavioral model. In order to improve the efficiency of this task, there is currently a great interest in using model-checking techniques developed within the area of computer aided verification. In this paper, we propose to represent the system as automata and we define a property called inversibility. This property is used to improve the efficiency of the search algorithm computing trajectories. We present two study cases in diagnosis and planning domains where this approach gives satisfactory results.