255 research outputs found
The Spectrum of Strong Behavioral Equivalences for Nondeterministic and Probabilistic Processes
We present a spectrum of trace-based, testing, and bisimulation equivalences
for nondeterministic and probabilistic processes whose activities are all
observable. For every equivalence under study, we examine the discriminating
power of three variants stemming from three approaches that differ for the way
probabilities of events are compared when nondeterministic choices are resolved
via deterministic schedulers. We show that the first approach - which compares
two resolutions relatively to the probability distributions of all considered
events - results in a fragment of the spectrum compatible with the spectrum of
behavioral equivalences for fully probabilistic processes. In contrast, the
second approach - which compares the probabilities of the events of a
resolution with the probabilities of the same events in possibly different
resolutions - gives rise to another fragment composed of coarser equivalences
that exhibits several analogies with the spectrum of behavioral equivalences
for fully nondeterministic processes. Finally, the third approach - which only
compares the extremal probabilities of each event stemming from the different
resolutions - yields even coarser equivalences that, however, give rise to a
hierarchy similar to that stemming from the second approach.Comment: In Proceedings QAPL 2013, arXiv:1306.241
Uniform Labeled Transition Systems for Nondeterministic, Probabilistic, and Stochastic Process Calculi
Labeled transition systems are typically used to represent the behavior of
nondeterministic processes, with labeled transitions defining a one-step state
to-state reachability relation. This model has been recently made more general
by modifying the transition relation in such a way that it associates with any
source state and transition label a reachability distribution, i.e., a function
mapping each possible target state to a value of some domain that expresses the
degree of one-step reachability of that target state. In this extended
abstract, we show how the resulting model, called ULTraS from Uniform Labeled
Transition System, can be naturally used to give semantics to a fully
nondeterministic, a fully probabilistic, and a fully stochastic variant of a
CSP-like process language.Comment: In Proceedings PACO 2011, arXiv:1108.145
A uniform framework for modelling nondeterministic, probabilistic, stochastic, or mixed processes and their behavioral equivalences
Labeled transition systems are typically used as behavioral models of concurrent processes, and the labeled transitions define the a one-step state-to-state reachability relation. This model can be made generalized by modifying the transition relation to associate a state reachability distribution, rather than a single target state, with any pair of source state and transition label. The state reachability distribution becomes a function mapping each possible target state to a value that expresses the degree of one-step reachability of that state. Values are taken from a preordered set equipped with a minimum that denotes unreachability. By selecting suitable preordered sets, the resulting model, called ULTraS from Uniform Labeled Transition System, can be specialized to capture well-known models of fully nondeterministic processes (LTS), fully
probabilistic processes (ADTMC), fully stochastic processes (ACTMC), and of nondeterministic and probabilistic (MDP) or nondeterministic and stochastic (CTMDP) processes. This uniform treatment of different behavioral models extends to behavioral equivalences. These can be defined on ULTraS by relying on appropriate measure functions that expresses the degree of reachability of a set of states when performing
single-step or multi-step computations. It is shown that the specializations of bisimulation, trace, and testing
equivalences for the different classes of ULTraS coincide with the behavioral equivalences defined in the literature over traditional models
Revisiting bisimilarity and its modal logic for nondeterministic and probabilistic processes
We consider PML, the probabilistic version of Hennessy-Milner logic introduced by Larsen and Skou to characterize bisimilarity over probabilistic processes without internal
nondeterminism.We provide two different interpretations for PML by considering nondeterministic and probabilistic processes as models, and we exhibit two new bisimulation-based equivalences that are in full agreement with those interpretations. Our new equivalences include
as coarsest congruences the two bisimilarities for nondeterministic and probabilistic processes proposed by Segala and Lynch. The latter equivalences are instead in agreement with two versions of Hennessy-Milner logic extended with an additional probabilistic operator
interpreted over state distributions rather than over individual states. Thus, our new interpretations of PML and the corresponding new bisimilarities offer a uniform framework for reasoning on processes that are purely nondeterministic or reactive probabilistic or are mixing nondeterminism and probability in an alternating/non-alternating way
Metrofission project: An overview of the ENEA contribution
Abstract The paper describes the progress made by ENEA-INMRI in the research carried out within the framework of the Joint Research Project (JRP) ENG08 Metrofission of the European Metrology Research Programme (EMRP). This JRP aims to solve important problems related to the development of the Generation IV (GenIV) nuclear power plants in Europe as required by new demands of energy supplies that must be secure, sustainable, of high quality and also able to reduce the green house gas emissions. In the Metrofission project the ENEA-INMRI is directly involved in three Working Packages (WPs): the WP6, that aims to develop a portable Triple-to-Double-Coincidence-Ratio (TDCR) system for measuring in-situ of pure beta radionuclides; the WP7, devoted to the development of modern acquisition techniques based on Digital Coincidence Counting (DCC) with high sampling speed for radionuclide standardization; the WP8, leaded by ENEA-INMRI, which takes into account the impact of the project toward the end-users and the nuclear industry. The new prototype of the ENEA-INMRI portable TDCR counter will be presented. The preliminary results obtained in the activity measurements of 14C and 63Ni standard sources carried out by the new counter equipped with the new front-end electronics based on the CAEN Digitizer DT5720 will be discussed
Process Discovery on Deviant Traces and Other Stranger Things
As the need to understand and formalise business processes into a model has grown over the last years, the process discovery research field has gained more and more importance, developing two different classes of approaches to model representation: procedural and declarative. Orthogonally to this classification, the vast majority of works envisage the discovery task as a one-class supervised learning process guided by the traces that are recorded into an input log.
In this work instead, we focus on declarative processes and embrace the less-popular view of process discovery as a binary supervised learning task, where the input log reports both examples of the normal system execution, and traces representing a āstrangerā behaviour according to the domain semantics. We therefore deepen how the valuable information brought by both these two sets can be extracted and formalised into a model that is āoptimalā according to user-defined goals. Our approach, namely NegDis, is evaluated w.r.t. other relevant works in this field, and shows promising results regarding both the performance and the quality of the obtained solution
Discovering Business Processes models expressed as DNF or CNF formulae of Declare constraints
In the field of Business Process Management, the Process Discovery task is one of the most important and researched topics. It aims to automatically learn process models starting from a given set of logged execution traces. The majority of the approaches employ procedural languages for describing the discovered models, but declarative languages have been proposed as well. In the latter category there is the Declare language, based on the notion of constraint, and equipped with a formal semantics on LTLf. Also, quite common in the field is to consider the log as a set of positive examples only, but some recent approaches pointed out that a binary classification task (with positive and negative examples) might provide better outcomes. In this paper, we discuss our preliminary work on the adaptation of some existing algorithms for Inductive Logic Programming, to the specific setting of Process Discovery: in particular, we adopt the Declare language with its formal semantics, and the perspective of a binary classification task (i.e., with positive and negative examples
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