Mémoires de Fin d’Etudes
Etablissement
Université de Sétif 1 - Ferhat Abbas
Affiliation
Département d’Informatique
Auteur
KARA épouse HAMDI-CHERIF)-MOHAMED, Chafia
Directeur de thèse
ABOUBAKEUR.HAMDI-CHERIF (Professeur)
Co-directeur
ABDALLAH. BOUKERRAM (Maitre de conférence)
Filière
Intelligence Artificielle et Images
Diplôme
Doctorat
Titre
A development environment integrating algorithms, inferences and learning – ESLIM Project
Mots clés
learning settings- a program is intended to infer (or induce) an unknown-Avoiding the “general problem solving (GPS)” syndrome-development-integrating algorithms.
Résumé
Most programming languages are based on context free grammars (CFGs). The purpose of grammatical inference is to infer a grammar, in our situation a CFG, from positive examples of sentences and possibly incorrect ones, for a given language. Based on these two fundamental definitions, we propose an environment followed by an implementation unifying different aspects of programming in machine learning settings. The central idea of this work is to use grammatical inference (GI) as a unifying framework for achieving this integration. Because any program can be considered as a string of characters, we show that the use of grammatical inference can not only unify different aspects of programming but also extend to wider areas of applications. The work sums up the following contributions: · State of the art of language theory and of grammatical inference; · Design and development of an environment integrating machine learning and first-order logic (FOL); · Design and development of a FOL system for parsing sentences independently or with a learning module; · Design and development of a heuristics-based polynomial-time complexity algorithm enhancing the learning process in grammatical inference. · Interaction between grammatical inference and control systems. The present work bears a promising line of research, contributing further to programming languages integration, aiming at the improvement of these languages with a machine learning layer.
Date de soutenance
2012
Cote
TH859
Pagination
186P
Format
CD
Statut
Soutenue