2019•Unpublished venueRequires access

Grammar Inference Based on Passive Learning and Genetic Algorithm

Petr Grachev

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Abstract

The mathematical model of a deterministic finite automaton has a wide potential of application, for instance, in control systems. Some of that systems are not trivial and can be defined only in terms of formal language theory. In this paper, we propose a new model for grammar inference, i.e. synthesizing of a deterministic finite automaton by a list of positive and negative examples. We present the results of testing developed model on formal grammars of various complexity.

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What this paper is about

The mathematical model of a deterministic finite automaton has a wide potential of application, for instance, in control systems. Some of that systems are not trivial and can be defined only in terms of formal language theory. In this paper, we propose a new model for grammar inference, i.e. synthesizing of a deterministic finite automaton by a list of positive and negative examples. We present the results of testing developed model on formal grammars of various complexity.

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Available abstract

The mathematical model of a deterministic finite automaton has a wide potential of application, for instance, in control systems. Some of that systems are not trivial and can be defined only in terms of formal language theory. In this paper, we propose a new model for grammar inference, i.e. synthesizing of a deterministic finite automaton by a list of positive and negative examples. We present the results of testing developed model on formal grammars of various complexity.

Key concepts: Grammar induction, Computer science, Formal grammar, Formal language, Rule-based machine translation, Grammar, Inference, Automaton

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