Transformations Between Different Models of Unranked Bottom-Up Tree Automata

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

Fundamenta Informaticae Pub Date : 2010-08-10 DOI:10.4204/EPTCS.31.18

Xiaoxue Piao, K. Salomaa

引用次数: 14

Abstract

We consider the representational state complexity of unranked tree automata. The bottom-up computation of an unranked tree automaton may be either deterministic or nondeterministic, and further variants arise depending on whether the horizontal string languages defining the transitions are represented by a DFA or an NFA. Also, we consider for unranked tree automata the alternative syntactic definition of determinism introduced by Cristau et al. (FCT'05, LNCS 3623, pp. 68-79). We establish upper and lower bounds for the state complexity of conversions between different types of unranked tree automata.

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无排序自底向上树形自动机不同模型之间的转换

我们考虑了未排序树自动机的表征状态复杂性。未排序树自动机的自底向上计算可能是确定的，也可能是不确定的，根据定义转换的水平字符串语言是由DFA还是NFA表示，还会出现更多的变体。此外，我们还考虑了由Cristau等人(FCT'05, LNCS 3623, pp. 68-79)引入的非排序树自动机的另一种决定论语法定义。建立了不同类型的无秩树自动机之间转换的状态复杂度的上界和下界。

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.