Automated Partitioning of Concurrent Discrete Event Systems for Distributed Behavioural Identification

Jeremie Saives (1) , Gregory Faraut (1) , Jean-Jacques Lesage (1)

1 LURPA - Laboratoire Universitaire de Recherche en Production Automatisée

The aim of behavioural identification of Discrete Event Systems is to build, from a sequence of observed in-puts/outputs events, an understandable model that exhibits both the direct relations between inputs and outputs events (i.e. the observable behaviour of the system) and the internal state evolutions (i.e. the unobservable behaviour). Since parallelism hinders the construction of monolithic models, distributed identification builds instead models of subsystems. This paper proposes an automated partitioning of the system, optimal regarding the readability of the identified distributed models, thus fitting reverse-engineering purposes. To solve the optimization problem, a first solution is extracted from the observable behaviour, then additional solutions are computed by agglomerative clustering. The approach is applied to a benchmark, resulting in an adequate functional partition.

Mots clés: Discrete-event systems; Petri-nets; Identification; Distributed models

Domaines: Automatique / Robotique

Jeremie Saives, Gregory Faraut, Jean-Jacques Lesage. Automated Partitioning of Concurrent Discrete Event Systems for Distributed Behavioural Identification. IEEE Transactions on Automation Science and Engineering, 2017, ⟨10.1109/TASE.2017.2718244⟩. ⟨hal-01564213⟩