Fuzzy context-free languages. Part 1: Generalized fuzzy context-free grammars

Motivated by aspects of robustness in parsing a context-free language, we study generalized fuzzy context-free grammars. These so-called fuzzy context-free $K$-grammars provide a very general framework to describe correctly as well as erroneously derived sentences by a single generating mechanism. They model the situation of making a finite choice out of an infinity of possible grammatical errors during each context-free derivation step. Formally, a fuzzy context-free $K$-grammar is a fuzzy context-free grammar with a countable rather than a finite number of rules satisfying the following condition: for each symbol $\alpha$, the set containing all right-hand sides of rules with left-hand side equal to $\alpha$ forms a fuzzy language that belongs to a given family $K$ of fuzzy languages. We investigate the generating power of fuzzy context-free $K$-grammars, and we show that under minor assumptions on the parameter $K$, the family of languages generated by fuzzy context-free $K$-grammars possesses closure properties very similar to those of the family of ordinary context-free languages

Fuzzy context-free languages. Part 1: Generalized fuzzy context-free grammars

Motivated by aspects of robustness in parsing a context-free language, we study generalized fuzzy context-free grammars. These fuzzy context-free K-grammars provide a general framework to describe correctly as well as erroneously derived sentences by a single generating mechanism. They model the situation of making a finite choice out of an infinity of possible grammatical errors during each context-free derivation step. Formally, a fuzzy context-free K-grammar is a fuzzy context-free grammar with a countable rather than a finite number of rules satisfying the following condition: for each symbol , the set containing all right-hand sides of rules with left-hand side equal to forms a fuzzy language that belongs to a given family K of fuzzy languages. We investigate the generating power of fuzzy context-free K-grammars, and we show that under minor assumptions on the parameter K, the family of languages generated by fuzzy context-free K-grammars possesses closure properties very similar to those of the family of ordinary context-free languages.\ud \u

Inferência de gramática formais livres de contexto utilizando computação evolucionária com aplicação em bioinformática

Grammatical inference deals with the task of learning a classifier that can recognize a particular pattern in a set of examples. In this work, a new grammatical inference model based on a variant of Genetic Programming is proposed. In this approach, an individual is a list of structured trees representing their productions. Ordinary genetic operators are modified so as to bias the search and two new operators are proposed. The first one, called Incremental Learning, is able to recognize, based on examples, which productions are missing. The second, called Expansion is able to provide the diversity necessary to achieve convergence. In a suite of experiments performed, the proposed model successfully inferred six regular grammars and two context-free grammars: parentheses and palindromes with four letters, including the disjunct one. Results achieved were better than those obtained by recently published algorithms. Nowadays, grammatical inference has been applied to problems of recognition of biological sequences of DNA. In this work, two problems of this class were addressed: recognition of promoters and splice junction detection. In the former, the proposed model obtained results better than other published approaches. In the latter, the proposed model showed promising results. The model was extended to support fuzzy grammars, namely the fuzzy fractional grammars. Furthermore, an appropriate method of estimation of the values of the production's membership function is also proposed. Results obtained in the identification of splice junctions shows the utility of the fuzzy inference model proposed.A inferência gramatical lida com o problema de aprender um classificador capaz de reconhecer determinada construção ou característica em um conjunto qualquer de exemplos. Neste trabalho, um modelo de inferência gramatical baseado em uma variante de Programação Genética é proposto. A representação de cada indivíduo é baseada em uma lista ligada de árvores representando o conjunto de produções da gramática. A atuação dos operadores genéticos é feita de forma heurística. Além disto, dois novos operadores genéticos são apresentados. O primeiro, denominado Aprendizagem Incremental, é capaz de reconhecer, com base em exemplos, quais regras de produção estão faltando. O segundo, denominado Expansão, é capaz de prover a diversidade necessária. Em experimentos efetuados, o modelo proposto inferiu com sucesso seis gramáticas regulares e duas gramáticas livres de contexto: parênteses e palíndromos de quatro letras, tanto o comum quanto o disjunto, sendo superior a abordagens recentes. Atualmente, modelos de inferência gramatical têm sido aplicados a problemas de reconhecimento de sequências biológicas de DNA. Neste trabalho, dois problemas de identificação de padrão foram abordados: reconhecimento de promotores e splice-junction. Para o primeiro, o modelo proposto obteve resultado superior a outras abordagens. Para o segundo, o modelo proposto apresentou bons resultados. O modelo foi estendido para o uso de gramáticas fuzzy, mais especificamente, as gramáticas fuzzy fracionárias. Para tal, um método de estimação adequado dos valores da função de pertinência das produções da gramática é proposto. Os resultados obtidos na identificação de splice-junctions comprovam a utilidade do modelo de inferência gramatical fuzzy proposto

Interim research assessment 2003-2005 - Computer Science

This report primarily serves as a source of information for the 2007 Interim Research Assessment Committee for Computer Science at the three technical universities in the Netherlands. The report also provides information for others interested in our research activities