Two-Sided Derivatives for Regular Expressions and for Hairpin Expressions

IF 0.4 4区计算机科学 Q4 COMPUTER SCIENCE, SOFTWARE ENGINEERING

Fundamenta Informaticae Pub Date : 2013-01-15 DOI:10.3233/FI-2015-1189

Jean-Marc Champarnaud, J. Dubernard, H. Jeanne, L. Mignot

引用次数: 8

Abstract

The aim of this paper is to design the polynomial construction of a finite recognizer for hairpin completions of regular languages. This is achieved by considering completions as new expression operators and by applying derivation techniques to the associated extended expressions called hairpin expressions. More precisely, we extend partial derivation of regular expressions to two-sided partial derivation of hairpin expressions and we show how to deduce a recognizer for a hairpin expression from its two-sided derived term automaton, providing an alternative proof of the fact that hairpin completions of regular languages are linear context-free.

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正则表达式和发夹表达式的双边导数

本文的目的是设计一个正则语言发夹补全有限识别器的多项式结构。这是通过将补全视为新的表达式操作符并将派生技术应用于称为发夹表达式的相关扩展表达式来实现的。更准确地说，我们将正则表达式的偏派生扩展到发夹表达式的双面偏派生，并展示了如何从其双面派生项自动机中推导出发夹表达式的识别器，提供了正则语言的发夹补全是线性上下文无关的事实的另一种证明。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Fundamenta Informaticae 工程技术-计算机：软件工程

CiteScore

2.00

自引率

0.00%

发文量

审稿时长

9.8 months

期刊介绍： Fundamenta Informaticae is an international journal publishing original research results in all areas of theoretical computer science. Papers are encouraged contributing: solutions by mathematical methods of problems emerging in computer science solutions of mathematical problems inspired by computer science. Topics of interest include (but are not restricted to): theory of computing, complexity theory, algorithms and data structures, computational aspects of combinatorics and graph theory, programming language theory, theoretical aspects of programming languages, computer-aided verification, computer science logic, database theory, logic programming, automated deduction, formal languages and automata theory, concurrency and distributed computing, cryptography and security, theoretical issues in artificial intelligence, machine learning, pattern recognition, algorithmic game theory, bioinformatics and computational biology, quantum computing, probabilistic methods, algebraic and categorical methods.