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2016 | OriginalPaper | Chapter

Modelling and Analysis of Collective Adaptive Systems with CARMA and its Tools

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Abstract

Collective Adaptive Systems (CAS) are heterogeneous collections of autonomous task-oriented systems that cooperate on common goals forming a collective system. This class of systems is typically composed of a huge number of interacting agents that dynamically adjust and combine their behaviour to achieve specific goals.
This chapter presents Carma, a language recently defined to support specification and analysis of collective adaptive systems, and its tools developed for supporting system design and analysis. Carma is equipped with linguistic constructs specifically developed for modelling and programming systems that can operate in open-ended and unpredictable environments. The chapter also presents the Carma Eclipse plug-in that allows Carma models to be specified by means of an appropriate high-level language. Finally, we show how Carma and its tools can be used to support specification with a simple but illustrative example of a socio-technical collective adaptive system.

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Footnotes
1
The precise syntax of expressions e has been deliberately omitted. We only assume that expressions are built using the appropriate combinations of values, attributes (sometime prefixed with \(\mathsf {my}\)), variables and the special term \(\mathsf {now}{}\). The latter is used to refer to current time unit.
 
2
Here we use \(\bullet \) to denote the unit value.
 
3
Here we assume that functions mtime and atime are obtained after some observations on real systems.
 
4
We let \(\llbracket \cdot \rrbracket _{\gamma }\) denote the evaluation function of an expression/predicate with respect to the store \(\gamma \).
 
5
All the operators are right associative and presented in the order of priority.
 
6
Detailed installation instructions can be found at http://​quanticol.​sourceforge.​net.
 
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Metadata
Title
Modelling and Analysis of Collective Adaptive Systems with CARMA and its Tools
Authors
Michele Loreti
Jane Hillston
Copyright Year
2016
DOI
https://doi.org/10.1007/978-3-319-34096-8_4

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