Nhóm nghiên cứu : APR

How can we interpret and what information can we extract of a large structure? More specifically, my goal consists in interpreting the syntactic and semantic structures as combinatorial classes in order to obtain quantitative and algorithmic information.

In this thesis, we will concentrate more on combinatorial models than on their applications. I want to highlight the evolutions of the models, explaining the contributions of each characteristic in the results. I presents several cases,where a more complex model does not fundamentally modify its guantitative behavior. These iterative studies, built on more and and more complex bricks, allow to understand the combinatorics underlying the more evolved models and the reasons why an approach or an algorithm cannot finally be adapted

M. Michael DRMOTA- Professeur & Technische Universität Wien (Vienne)-Rapporteur

M. Arnaud DURAND- Professeur & Université Denis Diderot (Paris)-Rapporteur

M. Conrado MARTINEZ -Professeur & Universitat Politècnica de Catalunya (Barcelone)-Rapporteur

Mme Béatrice BERARD-Professeur & Université Pierre et Marie Curie (Paris)- Examinateur

M. Olivier BODINI- Professeur & Université Paris-Nord (Paris)- Examinateur

M. Alain DENISE -Professeur & Université Paris-Sud (Paris)- Examinateur

M. Jean MAIRESSE - Directeur de Recherche CNRS & Université Pierre et Marie Curie (Paris)- Examinateur

M. Cyril NICAUD- Professeur & Université Paris-Est, Marne-la-Vallée (Paris)- Examinateur

Mme Michèle SORIA-Professeur & Université Pierre et Marie Curie (Paris)- Examinateur

- PEPIN Martin : Génération aléatoire uniforme de trajectoires dans les systèmes concurrents