Generating Minimal Nondeterministic Finite Automata Using a Parallel Algorithm

The goal of this paper is to develop a parallel algorithm that, on input of a learning sample, identifies a regular language by means of a nondeterministic finite automaton (NFA). A sample is a pair of finite sets containing positive and negative examples. Given a sample, a minimal NFA or the range...

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Bibliographic Details
Published in2020 19th International Symposium on Parallel and Distributed Computing (ISPDC) pp. 37 - 43
Main Authors Jastrzab, Tomasz, Czech, Zbigniew J., Wieczorek, Wojciech
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2020
Subjects
Automata
constraint satisfaction and satisfiability problems
grammatical inference
Inference algorithms
Learning automata
learning regular languages using nondeterministic finite automata
parallel algorithm
Parallel algorithms
Software
Software algorithms
Task analysis
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DOI10.1109/ISPDC51135.2020.00015

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Summary:The goal of this paper is to develop a parallel algorithm that, on input of a learning sample, identifies a regular language by means of a nondeterministic finite automaton (NFA). A sample is a pair of finite sets containing positive and negative examples. Given a sample, a minimal NFA or the range of possible sizes of such an NFA, that represents the target regular language is sought. We define the task of finding an NFA, which accepts all positive examples and rejects all negative ones, as a constraint satisfaction problem, and then propose a parallel algorithm to solve the problem. The results of computational experiments on the variety of test samples are reported.
DOI:10.1109/ISPDC51135.2020.00015