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Causal Closure for MSC Languages

Authors: Bharat Adsul; Madhavan Mukund; K. Narayan Kumar; Vasumathi Narayanan;

Causal Closure for MSC Languages

Abstract

Message sequence charts (MSCs) are commonly used to specify interactions between agents in communicating systems. Their visual nature makes them attractive for describing scenarios, but also leads to ambiguities that can result in incomplete or inconsistent descriptions. One such problem is that of implied scenarios—a set of MSCs may imply new MSCs which are “locally consistent” with the given set. If local consistency is defined in terms of local projections of actions along each process, it is undecidable whether a set of MSCs is closed with respect to implied scenarios, even for regular MSC languages [3]. We introduce a new and natural notion of local consistency called causal closure, based on the causal view of a process—all the information it collects, directly or indirectly, through its actions. Our main result is that checking whether a set of MSCs is closed with respect to implied scenarios modulo causal closure is decidable for regular MSC languages.

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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!
15
Average
Top 10%
Average
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