Профессор Штефан Хаар в гостях у лаборатории ПОИС
Темы выступлений и краткие аннотации приводятся далее.
Seminar I (Monday, Sep 15, 18:00-19:30, auditorium 402)
What Occurrence Nets Reveal
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Occurrence nets are a well known partial order model for the concurrent behavior of Petri nets, and provide a useful tool to tackle state-space explosion in verification and related tasks. Moreover, their structure allows to access directly the relations of causal precedence, concurrency, and conflict between events. By exploring the data structure further, one finds that event a may reveal event b in the sense that occurrence of a implies that b inevitably occurs, too, be it before, after, or concurrently with a. Knowledge of reveals facilitates in particular the analysis of partially observable systems, in the context of diagnosis, testing, or verification; it can also be used to generate more concise representations of behaviours via abstractions.This binary reveals relation has been shown decidable: for a given pair a,b in the unfolding U of a safe Petri net N, a finite prefix P of U is sufficient to decide whether or not a reveals b.Beyond these binary structural relations, we generalize the reveals relation to express more general dependencies, involving more than two events, and indicate some applications, including diagnosis to be treated in the second seminar.
Seminar II (Monday, Sep 22, 18:00-19:30, auditorium 402)
Reveal your faults!
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From a partial observation of the behaviour of a labeled Discrete Event System, fault diagnosis is the task of detecting whether or not the given sequence of observed labels indicates that some unobservable fault has occurred. Diagnosability is an associated system property, stating that in any possible execution an occurrence of a fault can eventually be diagnosed.
We will show some facets of diagnosis and diagnosability from recent work: 1) Active diagnosis aims at controlling the system in order to make it diagnosable. With a dedicated automata construction, we can solve the active diagnosability decision problem and the active diagnoser synthesis problem in a way that improves over previous solution, in that (a) our procedures are optimal w.r.t. to computational complexity, and (b) the memory required for the active diagnoser produced by the synthesis is minimal. If time allows, stochastic extensions and ideas for future work can also be discussed. 2) When diagnosis is considered in the context of concurrent systems, partial order semantics adds to the difficulty of the problem, but also provides a richer and more complex picture of observation and diagnosis. In *weak* diagnosis, one asks whether a concurrent chronicle of observed events allows to determine that a non-observable fault will inevitably occur, sooner or later, on any maximal system run compatible with the observation. Under the assumption that the system is modeled by a safe Petri net, it suffices to compute suitable finite unfolding prefixes of bounded size, to obtain sufficient information for the diagnosis algorithm. Our work extends and generalizes the unfolding-based diagnosis approaches by Benveniste et al (2003 etc) as well as Esparza and Kern (2012).
Colloquium (Thursday, Sep 18, 16:40 - 18:00, Dekart lecture hall)
True concurrency - from C.A. Petri to Telecom and Systems Biology
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Some circles, squares and arrows, plus some black dots moving along: that is all it takes to build a Petri net. These nets are a mathematical tool and model for dynamical systems generally considered to be "at home" in computer science. However, a great deal of the theory of Petri nets and of the concurrency (describing asynchronous parallel processes) which they involve, had been developed for and inspired by the understanding of physical processes, building upon principles from chemical reactions, and both relativistic and quantum physics. It is fair to say that Petri nets are not only intuitive, but also fertile for many fields; in this talk, I will illustrate this in the contexts of physics, engineering, and biology, reflecting in some sort the evolution that the field has taken over the past 50 years.
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