Diagnosis of Active Systems with Abstract Observations and Compiled Knowledge

An active system (AS) is a discrete-event system (DES) with asynchronous behavior, which is represented by a network of components that are modeled as communicating automata. When being operated, an AS performs a trajectory within its behavior space, while generating a sequence of observations, namely a temporal observation. The model of the AS and a temporal observation are the two key ingredients of the diagnosis task, which aims to find out possible faulty behavior via abductive reasoning. Among other knowledge, such reasoning requires knowing what is observable and what is not. This essential distinction constitutes the observability of the AS. In the literature, the observability of a DES boils down to qualifying each state transition either as observable or unobservable, which contrasts with the way humans observe reality, typically by mapping a collection of observations to a single, abstract perception. Moreover, the occurrence of single state transitions is not necessarily what we can observe or what we want to observe for diagnosis purposes. This paper presents an extended notion of observability, where each observation is associated with a behavioral scenario rather than a single state transition, where a scenario is defined as a regular language on state transitions. To speed up the online diagnosis engine, specific diagnosis-oriented knowledge is compiled offline. Eventually, the diagnosis technique based on abstract observability is extended to cope with temporal uncertainty.

Asynchronous session subtyping as communicating automata refinement

We study the relationship between session types and behavioural contracts, representing Communicating Finite State Machines (CFSMs), under the assumption that processes communicate asynchronously. Session types represent a syntax-based approach for the description of communication protocols, while behavioural contracts, formally expressing CFSMs, follow an operational approach. We show the existence of a fully abstract interpretation of session types into a fragment of contracts that maps session subtyping into binary compliance-preserving CFSMs/behavioural contract refinement. In this way, on the one hand, we enrich the theory of session types with an operational characterization and, on the other hand, we use recent undecidability results for asynchronous session subtyping to obtain an original undecidability result for asynchronous CFSMs/behavioural contract refinement.

Diagnosis of Deep Discrete-Event Systems

An abduction-based diagnosis technique for a class of discrete-event systems (DESs), called deep DESs (DDESs), is presented. A DDES has a tree structure, where each node is a network of communicating automata, called an active unit (AU). The interaction of components within an AU gives rise to emergent events. An emergent event occurs when specific components collectively perform a sequence of transitions matching a given regular language. Any event emerging in an AU triggers the transition of a component in its parent AU. We say that the DDES has a deep behavior, in the sense that the behavior of an AU is governed not only by the events exchanged by the components within the AU but also by the events emerging from child AUs. Deep behavior characterizes not only living beings, including humans, but also artifacts, such as robots that operate in contexts at varying abstraction levels. Surprisingly, experimental results indicate that the hierarchical complexity of the system translates into a decreased computational complexity of the diagnosis task. Hence, the diagnosis technique is shown to be (formally) correct as well as (empirically) efficient.