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zbMATH Open
Article . 2011
Data sources: zbMATH Open
Fundamenta Informaticae
Article . 2011
Data sources: mEDRA
DBLP
Article . 2011
Data sources: DBLP
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Rough Approximations in Varieties of Regular Languages

Rough approximations in varieties of regular languages
Authors: Gabriela Martín; Magnus Steinby;

Rough Approximations in Varieties of Regular Languages

Abstract

We study approximations of regular languages bymembers of a given variety ℒ of regular languages. These are upper or lower approximations in the sense of Pawlak's rough set theory with respect to congruences belonging to the variety of congruences corresponding to ℒ. In particular, we consider the closest upper and lower approximations in ℒ. In so-called principal varieties these always exist, and we present algorithms for finding them, but for other varieties the situation is more complex. Although we consider just Eilenberg’s +-varieties, the general ideas apply also to other types of varieties of languages. Our work may also be viewed as an approach to the characterizable inference problem in which a language of a certain kind is to be inferred from a given sample.

Keywords

varieties of languages, Formal languages and automata, finite automata, rough approximations, regular 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!
0
Average
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