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Parallel Algorithms for Minimal Nondeterministic Finite Automata Inference

FUNDAMENTA INFORMATICAE 2021年第3期178卷 203-227页

作者： Jastrzab, Tomasz Czech, Zbigniew J. Wieczorek, Wojciech Silesian Tech Univ Gliwice Poland Univ Bielsko Biala Bielsko Biala Poland

The goal of this paper is to develop the parallel algorithms that, on input of a learning sample, identify 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 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 the parallel algorithms to solve the problem. The results of comprehensive computational experiments on the variety of inference tasks are reported. The question of minimizing an NFA consistent with a learning sample is computationally hard.

关键词： parallel algorithms learning regular languages using nondeterministic finite automata constraint satisfaction and satisfiability problems grammatical inference

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Generating Minimal Nondeterministic Finite Automata Using a Parallel Algorithm 19

Generating Minimal Nondeterministic Finite Automata Using a ...

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19th International Symposium on Parallel and Distributed Computing (ISPDC)

作者： Jastrzab, Tomasz Czech, Zbigniew J. Wieczorek, Wojciech Silesian Tech Univ Dept Algorithm & Software Gliwice Poland Silesia Univ Katowice Fac Sci & Technol Katowice Poland

ISBN: (纸本)9781728189468

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.

关键词： parallel algorithm learning regular languages using nondeterministic finite automata constraint satisfaction and satisfiability problems grammatical inference

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