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Operational and compositional semantics of synchronous automaton compositions

Authors: Florence Maraninchi;

Operational and compositional semantics of synchronous automaton compositions

Abstract

The state/transition paradigm has been used extensively for the description of event-driven, parallel systems. However, the lack for hierarchic structure in such descriptions usually prevents us from using this paradigm in a real programming language. We propose the Argos language for reactive systems. The basic components of a program are input/output-labeled transition systems verifying reactivity (a property similar to input-enabling in IO-automata). The composition operations (parallel composition and refinement, providing hierarchy) are based upon the synchronous broadcast mechanism of Esterel. We define the language formally in an algebraic framework, and give an operational semantics. The main result is the compositionality of the semantics; we prove that the bisimulation of models induces an equivalence which is a congruence for the operators we propose. An interesting point is the way we introduce hierarchy in a compositional way.

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Powered by OpenAIRE graph
Found an issue? Give us feedback
selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
65
Top 10%
Top 1%
Top 1%
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