Probabilistic Linda-Based Coordination Languages

Pierro, Alessandra Di; Hankin, Chris; Wiklicky, Herbert

doi:10.1007/11561163_6

Cited by 4 publications

(3 citation statements)

References 32 publications

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“…Instead, (bio)chemical tuple spaces, as defined in [40,41], fully exploit the chemical metaphor by providing time-dependent and stochastic chemical laws; as a result, they are perfectly capable of fully reproducing the dynamics of a real chemical reaction and also of putting it to use in the coordination of complex adaptive pervasive systems [89]. This is also why probabilistic extensions to classical coordination models are going to be more and more relevant, following already existing examples such as the aforementioned stoklaim [85,90]-a stochastic extension of the klaim model for mobile coordination [91]-as well as the many probabilistic extensions of linda [92]-among which plinda [86] and Probabilistic klaim [93]. Formally, this will also require probabilistic formal models for the specification of the semantics of probabilistic coordination models, ISRN Software Engineering 7 possibly by combining already existing approaches-such as [45,46,49,90]-with newly developed ones.…”

Section: Expressing the Full Dynamics Of Natural Systemsmentioning

confidence: 99%

Nature-Inspired Coordination Models: Current Status and Future Trends

Omicini

2013

ISRN Software Engineering

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Coordination models and languages are meant to provide abstractions and mechanisms to harness the space of interaction as one of the foremost sources of complexity in computational systems. Nature-inspired computing aims at understanding the mechanisms and patterns of complex natural systems in order to bring their most desirable features to computational systems. Thus, the promise of nature-inspired coordination models is to prove themselves fundamental in the design of complex computational systems|such as intelligent, knowledge-intensive, pervasive, adaptive, and self-organising ones. In this paper, we survey the most relevant nature-inspired coordination models in the literature, focussing in particular on tuple-based models, and foresee the most interesting research trends in the field.

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Section: Expressing the Full Dynamics Of Natural Systemsmentioning

confidence: 99%

Nature-Inspired Coordination Models: Current Status and Future Trends

Omicini

2013

ISRN Software Engineering

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“…[12][13][14], functional ones, e.g. [15][16][17], but also declarative ones, like [5,18]. Besides this there is also a substantial work in probabilistic process algebras [19,20].…”

Section: Probabilistic Semanticsmentioning

confidence: 99%

Probabilistic Abstract Interpretation: From Trace Semantics to DTMC’s and Linear Regression

Pierro

Wiklicky

2015

Semantics, Logics, and Calculi

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Abstract. In order to perform probabilistic program analysis we need to consider probabilistic languages or languages with a probabilistic semantics, as well as a corresponding framework for the analysis which is able to accomodate probabilistic properties and properties of probabilistic computations. To this purpose we investigate in this paper the relationship between three different types of probabilisitic semantics for a core imperative language, namely Kozen's Fixpoint Semantics (KFS), our Linear Operator Semantics (LOS) as well as probabilistic versions of Maximal Trace Semantics (MTS). We also discuss the relationship between Probabilistic Abstract Interpretation (PAI) and statistical or linear regression analysis. While classical Abstract Interpretation, based on Galois connection, allows only for worst-case analyses, the use of the Moore-Penrose pseudo inverse in PAI opens the possibility of exploiting statistical and noisy observations in order to analyse and identify various system properties.

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“…From the modeling point of view, PRISM [19] is another probabilistic modeling language that comes with probabilistic modelchecking and quantitative analysis tools [18]. Some process algebraic approaches to modeling, verification, and analysis of probabilistic models are the PEPA [13] and EMPA [5] frameworks, the Probabilistic KLAIM coordination language [25], and the Stochastic π calculus [27]. PMaude, the probabilistic extension of Maude, is a rewrite-based modeling language.…”

Section: Introductionmentioning

confidence: 99%

MULE-Based Wireless Sensor Networks: Probabilistic Modeling and Quantitative Analysis

Kazemeyni

Johnsen

Owe

et al. 2012

Lecture Notes in Computer Science

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Abstract. Wireless sensor networks (WSNs) consist of resource-constrained nodes; especially with respect to power. In most cases, the replacement of a dead node is difficult and costly. It is therefore crucial to minimize the total energy consumption of the network. Since the major consumer of power in WSNs is the data transmission process, we consider nodes which cooperate for data transmission in terms of groups. A group has a leader which collects data from the members and communicates with the outside of the group. We propose and formalize a model for data collection in which mobile entities, called data MULEs, are used to move between group leaders and collect data messages using short-range and low-power data transmission. We combine declarative and operational modeling. The declarative model abstractly captures behavior without committing to specific transitions by means of probability distributions, whereas the operational model is given as a concrete transition system in rewriting logic. The probabilistic, declarative model is not used to select transition rules, but to stochastically capture the result of applying rules. Technically, we use probabilistic rewriting logic and embed our models into PMaude, which gives us a simulation engine for the combined models. We perform statistical quantitative analysis based on repeated discrete-event simulations in Maude.

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Probabilistic Linda-Based Coordination Languages

Cited by 4 publications

References 32 publications

Nature-Inspired Coordination Models: Current Status and Future Trends

Nature-Inspired Coordination Models: Current Status and Future Trends

Probabilistic Abstract Interpretation: From Trace Semantics to DTMC’s and Linear Regression

MULE-Based Wireless Sensor Networks: Probabilistic Modeling and Quantitative Analysis

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