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Conference object . 2011
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https://doi.org/10.1109/lics.2...
Article . 2011 . Peer-reviewed
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Qualitative Tree Languages

Authors: Carayol, Arnaud; Haddad, Axel; Serre, Olivier;

Qualitative Tree Languages

Abstract

We study finite automata running over infinite binary trees and we relax the notion of accepting run by allowing a negligible set (in the sense of measure theory) of non-accepting branches. In this qualitative setting, a tree is accepted by the automaton if there exists a run over this tree in which almost every branch is accepting. This leads to a new class of tree languages, called the qualitative tree languages that enjoys many properties. Then, we replace the existential quantification -- a tree is accepted if there exists some accepting run over the input tree -- by a probabilistic quantification -- a tree is accepted if almost every run over the input tree is accepting. Together with the qualitative acceptance and the Buchi condition, we obtain a class of probabilistic tree automata with a decidable emptiness problem. To our knowledge, this is the first positive result for a class of probabilistic automaton over infinite trees.

Country
France
Keywords

[INFO.INFO-CL] Computer Science [cs]/Computation and Language [cs.CL], Tree Automata, Probabilistic Automata, Markov Decision Process

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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
Average
Average
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