Section: Research Program
Concurrency
Participants : Thomas Chatain, Stefan Haar, Serge Haddad, Stefan Schwoon.

Glossary
 Concurrency:
Property of systems allowing some interacting processes to be executed in parallel.
 Diagnosis:
The process of deducing from a partial observation of a system aspects of the internal states or events of that system; in particular, fault diagnosis aims at determining whether or not some nonobservable fault event has occurred.
 Conformance Testing:
Feeding dedicated input into an implemented system $IS$ and deducing, from the resulting output of $I$, whether $I$ respects a formal specification $S$.
Introduction
It is well known that, whatever the intended form of analysis or control, a global view of the system state leads to overwhelming numbers of states and transitions, thus slowing down algorithms that need to explore the state space. Worse yet, it often blurs the mechanics that are at work rather than exhibiting them. Conversely, respecting concurrency relations avoids exhaustive enumeration of interleavings. It allows us to focus on `essential' properties of nonsequential processes, which are expressible with causal precedence relations. These precedence relations are usually called causal (partial) orders. Concurrency is the explicit absence of such a precedence between actions that do not have to wait for one another. Both causal orders and concurrency are in fact essential elements of a specification. This is especially true when the specification is constructed in a distributed and modular way. Making these ordering relations explicit requires to leave the framework of state/interleaving based semantics. Therefore, we need to develop new dedicated algorithms for tasks such as conformance testing, fault diagnosis, or control for distributed discrete systems. Existing solutions for these problems often rely on centralized sequential models which do not scale up well.
Diagnosis
Participants : Stefan Haar, Serge Haddad, Stefan Schwoon.
Fault Diagnosis for discrete event systems is a crucial task in automatic control. Our focus is on event oriented (as opposed to state oriented) modelbased diagnosis, asking e.g. the following questions:
given a  potentially large  alarm pattern formed of observations,

what are the possible fault scenarios in the system that explain the pattern ?

Based on the observations, can we deduce whether or not a certain  invisible  fault has actually occurred ?
Modelbased diagnosis starts from a discrete event model of the observed system  or rather, its relevant aspects, such as possible fault propagations, abstracting away other dimensions. From this model, an extraction or unfolding process, guided by the observation, produces recursively the explanation candidates.
In asynchronous partialorder based diagnosis with Petri nets [49], [50], [51], one unfolds the labelled product of a Petri net model $\mathcal{N}$ and an observed alarm pattern $\mathcal{A}$, also in Petri net form. We obtain an acyclic net giving partial order representation of the behaviors compatible with the alarm pattern. A recursive online procedure filters out those runs (configurations) that explain exactly $\mathcal{A}$. The Petrinet based approach generalizes to dynamically evolving topologies, in dynamical systems modeled by graph grammars, see [38]
Observability and Diagnosability
Diagnosis algorithms have to operate in contexts with low observability, i.e., in systems where many events are invisible to the supervisor. Checking observability and diagnosability for the supervised systems is therefore a crucial and nontrivial task in its own right. Analysis of the relational structure of occurrence nets allows us to check whether the system exhibits sufficient visibility to allow diagnosis. Developing efficient methods for both verification of diagnosability checking under concurrency, and the diagnosis itself for distributed, composite and asynchronous systems, is an important field for MExICo.
Distribution
Distributed computation of unfoldings allows one to factor the unfolding of the global system into smaller local unfoldings, by local supervisors associated with subnetworks and communicating among each other. In [50], [40], elements of a methodology for distributed computation of unfoldings between several supervisors, underwritten by algebraic properties of the category of Petri nets have been developed. Generalizations, in particular to Graph Grammars, are still do be done.
Computing diagnosis in a distributed way is only one aspect of a much vaster topic, that of distributed diagnosis (see [47], [53]). In fact, it involves a more abstract and often indirect reasoning to conclude whether or not some given invisible fault has occurred. Combination of local scenarios is in general not sufficient: the global system may have behaviors that do not reveal themselves as faulty (or, dually, nonfaulty) on any local supervisor's domain (compare [37], [43]). Rather, the local diagnosers have to join all information that is available to them locally, and then deduce collectively further information from the combination of their views. In particular, even the absence of fault evidence on all peers may allow to deduce fault occurrence jointly, see [55], [56]. Automatizing such procedures for the supervision and management of distributed and locally monitored asynchronous systems is a longterm goal to which MExICo hopes to contribute.
Hybrid Systems
Participants : Laurent Fribourg, Serge Haddad.
Hybrid systems constitute a model for cyberphysical systems which integrates continuoustime dynamics (modes) governed by differential equations, and discrete transitions which switch instantaneously from one mode to another. Thanks to their ease of programming, hybrid systems have been integrated to power electronics systems, and more generally in cyberphysical systems. In order to guarantee that such systems meet their specifications, classical methods consist in finitely abstracting the systems by discretization of the (infinite) state space, and deriving automatically the appropriate mode control from the specification using standard graph techniques. These methods face the wellknown problem of “curse of dimensionality”, and cannot generally treat systems of dimension exceeding 5 or 6. Thanks to the introduction of original compositional techniques [25], [30], [13] as well as finer estimations of integration errors [3], we are now able to control several case studies of greater dimension. Actually, in the real world, many parameters of hybrid models are not known precisely, and require adjustements to experimental data. We plan to elaborate methods based on parameter estimation and machine learning techniques in order to define formal stability criteria and wellposed learning problems in the framework of hybrid systems with nonlinear dynamics.
Contextual Nets
Participant : Stefan Schwoon.
Assuring the correctness of concurrent systems is notoriously difficult due to the many unforeseeable ways in which the components may interact and the resulting statespace explosion. A wellestablished approach to alleviate this problem is to model concurrent systems as Petri nets and analyse their unfoldings, essentially an acyclic version of the Petri net whose simpler structure permits easier analysis [48].
However, Petri nets are inadequate to model concurrent read accesses to the same resource. Such situations often arise naturally, for instance in concurrent databases or in asynchronous circuits. The encoding tricks typically used to model these cases in Petri nets make the unfolding technique inefficient. Contextual nets, which explicitly do model concurrent read accesses, address this problem. Their accurate representation of concurrency makes contextual unfoldings up to exponentially smaller in certain situations. An abstract algorithm for contextual unfoldings was first given in [39]. In recent work, we further studied this subject from a theoretical and practical perspective, allowing us to develop concrete, efficient data structures and algorithms and a tool (Cunf) that improves upon existing state of the art. This work led to the PhD thesis of César Rodríguez in 2014 .
Contextual unfoldings deal well with two sources of statespace explosion: concurrency and shared resources. Recently, we proposed an improved data structure, called contextual merged processes (CMP) to deal with a third source of statespace explosion, i.e. sequences of choices. The work on CMP [57] is currently at an abstract level. In the short term, we want to put this work into practice, requiring some theoretical groundwork, as well as programming and experimentation.
Another wellknown approach to verifying concurrent systems is partialorder reduction, exemplified by the tool SPIN. Although it is known that both partialorder reduction and unfoldings have their respective strengths and weaknesses, we are not aware of any conclusive comparison between the two techniques. Spin comes with a highlevel modeling language having an explicit notion of processes, communication channels, and variables. Indeed, the reduction techniques implemented in Spin exploit the specific properties of these features. On the other side, while there exist highly efficient tools for unfoldings, Petri nets are a relatively general lowlevel formalism, so these techniques do not exploit properties of higher language features. Our work on contextual unfoldings and CMPs represents a first step to make unfoldings exploit richer models. In the long run, we wish raise the unfolding technique to a suitable highlevel modelling language and develop appropriate tool support.