Investigating Fluid-Flow Semantics of Asynchronous Tuple-Based Process Languages for Collective Adaptive Systems - Coordination Models and Languages Access content directly
Conference Papers Year : 2015

Investigating Fluid-Flow Semantics of Asynchronous Tuple-Based Process Languages for Collective Adaptive Systems

Abstract

Recently, there has been growing interest in nature-inspired interaction paradigms for Collective Adaptive Systems, for modelling and implementation of adaptive and context-aware coordination, among which the promising pheromone-based interaction paradigm. System modelling in the context of such a paradigm may be facilitated by the use of languages in which adaptive interaction is decoupled in time and space through asynchronous buffered communication, e.g. asynchronous, repository- or tuple-based languages. In this paper we propose a differential semantics for such languages. In particular, we consider an asynchronous, repository based modelling kernel-language which is a restricted version of LINDA, extended with stochastic information about action duration. We provide stochastic formal semantics for both an agent-based view and a population-based view. We then derive an ordinary differential equation semantics from the latter, which provides a fluid-flow deterministic approximation for the mean behaviour of large populations. We show the application of the language and the ODE analysis on a benchmark example of foraging ants.
Fichier principal
Vignette du fichier
978-3-319-19282-6_2_Chapter.pdf (797.46 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-01774942 , version 1 (24-04-2018)

Licence

Attribution

Identifiers

Cite

Diego Latella, Michele Loreti, Mieke Massink. Investigating Fluid-Flow Semantics of Asynchronous Tuple-Based Process Languages for Collective Adaptive Systems. 17th International Conference on Coordination Languages and Models (COORDINATION), Jun 2015, Grenoble, France. pp.19-34, ⟨10.1007/978-3-319-19282-6_2⟩. ⟨hal-01774942⟩
301 View
111 Download

Altmetric

Share

Gmail Facebook X LinkedIn More