Distribution of Behaviour into Parallel Communicating Subsystems

Abstract

The process of decomposing a complex system into simpler subsystems has been of interest to computer scientists over many decades, most recently for the field of distributed computing. In this paper, motivated by the desire to distribute the process of active automata learning onto multiple subsystems, we study the equivalence between a system and the total behaviour of its decomposition which comprises subsystems with communication between them. We show synchronouslyand asynchronously-communicating decompositions that maintain branching bisimilarity, and we prove that there is no decomposition operator by our definition that maintains divergence-preserving branching bisimilarity over all LTSs.

Active Learning of Decomposable Systems

“…This work was motivated by a real case example in the industry [3] based on which we pursued the possibility of applying the active learning technique in parts. In [2], we studied the decomposability of systems based on input partitioning and have proven the validity of certain decompositions. This work continues the study on learning in parts by setting and proving constraints on systems under learning.…”