6 research outputs found
SOS rule formats for convex and abstract probabilistic bisimulations
Probabilistic transition system specifications (PTSSs) in the format provide structural operational semantics for
Segala-type systems that exhibit both probabilistic and nondeterministic
behavior and guarantee that bisimilarity is a congruence for all operator
defined in such format. Starting from the
format, we obtain restricted formats that guarantee that three coarser
bisimulation equivalences are congruences. We focus on (i) Segala's variant of
bisimulation that considers combined transitions, which we call here "convex
bisimulation"; (ii) the bisimulation equivalence resulting from considering
Park & Milner's bisimulation on the usual stripped probabilistic transition
system (translated into a labelled transition system), which we call here
"probability obliterated bisimulation"; and (iii) a "probability abstracted
bisimulation", which, like bisimulation, preserves the structure of the
distributions but instead, it ignores the probability values. In addition, we
compare these bisimulation equivalences and provide a logic characterization
for each of them.Comment: In Proceedings EXPRESS/SOS 2015, arXiv:1508.0634
Compositional bisimulation metric reasoning with Probabilistic Process Calculi
We study which standard operators of probabilistic process calculi allow for
compositional reasoning with respect to bisimulation metric semantics. We argue
that uniform continuity (generalizing the earlier proposed property of
non-expansiveness) captures the essential nature of compositional reasoning and
allows now also to reason compositionally about recursive processes. We
characterize the distance between probabilistic processes composed by standard
process algebra operators. Combining these results, we demonstrate how
compositional reasoning about systems specified by continuous process algebra
operators allows for metric assume-guarantee like performance validation
Semantics and expressiveness of ordered SOS
AbstractStructured Operational Semantics (SOS) is a popular method for defining semantics by means of transition rules. An important feature of SOS rules is negative premises, which are crucial in the definitions of such phenomena as priority mechanisms and time-outs. However, the inclusion of negative premises in SOS rules also introduces doubts as to the preferred meaning of SOS specifications.Orderings on SOS rules were proposed by Phillips and Ulidowski as an alternative to negative premises. Apart from the definition of the semantics of positive GSOS rules with orderings, the meaning of more general types of SOS rules with orderings has not been studied hitherto. This paper presents several candidates for the meaning of general SOS rules with orderings and discusses their conformance to our intuition for such rules.We take two general frameworks (rule formats) for SOS with negative premises and SOS with orderings, and present semantics-preserving translations between them with respect to our preferred notion of semantics. Thanks to our semantics-preserving translation, we take existing congruence meta-results for strong bisimilarity from the setting of SOS with negative premises into the setting of SOS with orderings. We further compare the expressiveness of rule formats for SOS with orderings and SOS with negative premises. The paper contains also many examples that illustrate the benefits of SOS with orderings and the properties of the presented definitions of meaning
Probabilistic Semantics: Metric and Logical Character\ua8ations for Nondeterministic Probabilistic Processes
In this thesis we focus on processes with nondeterminism and probability in the PTS model, and we propose novel techniques to study their semantics, in terms of both classic behavioral relations and the more recent behavioral metrics.
Firstly, we propose a method for decomposing modal formulae in a probabilistic extension of the Hennessy-Milner logic. This decomposition method allows us to derive the compositional properties of probabilistic (bi)simulations.
Then, we propose original notions of metrics measuring the disparities in the behavior of processes with respect to (decorated) trace and testing semantics.
To capture the differences in the expressive power of the metrics we order them by the relation `makes processes further than'.
Thus, we obtain the first spectrum of behavioral metrics on the PTS model.
From this spectrum we derive an analogous one for the kernels of the metrics, ordered by the relation `makes strictly less identification than'.
Finally, we introduce a novel technique for the logical characterization of both behavioral metrics and their kernels, based on the notions of mimicking formula and distance on formulae.
This kind of characterization allows us to obtain the first example of a spectrum of distances on processes obtained directly from logics.
Moreover, we show that the kernels of the metrics can be characterized by simply comparing the mimicking formulae of processes
Probabilistic Semantics: Metric and Logical Character¨ations for Nondeterministic Probabilistic Processes
In this thesis we focus on processes with nondeterminism and probability in the PTS model, and we propose novel techniques to study their semantics, in terms of both classic behavioral relations and the more recent behavioral metrics.
Firstly, we propose a method for decomposing modal formulae in a probabilistic extension of the Hennessy-Milner logic. This decomposition method allows us to derive the compositional properties of probabilistic (bi)simulations.
Then, we propose original notions of metrics measuring the disparities in the behavior of processes with respect to (decorated) trace and testing semantics.
To capture the differences in the expressive power of the metrics we order them by the relation `makes processes further than'.
Thus, we obtain the first spectrum of behavioral metrics on the PTS model.
From this spectrum we derive an analogous one for the kernels of the metrics, ordered by the relation `makes strictly less identification than'.
Finally, we introduce a novel technique for the logical characterization of both behavioral metrics and their kernels, based on the notions of mimicking formula and distance on formulae.
This kind of characterization allows us to obtain the first example of a spectrum of distances on processes obtained directly from logics.
Moreover, we show that the kernels of the metrics can be characterized by simply comparing the mimicking formulae of processes