As reações químicas que resultam da expressão de genes são complexas e ainda não são total- mente compreendidas. Sabe-se que os genes enviam, recebem, e processam informações formando uma complexa rede de comunicação, mas a arquitetura e dinâmica destas redes não são totalmente conhecidas. Dessa forma, um problema importante é determinar como os genes se relacionam dentro da célula. Esse processo de determinar o relacionamento entre os genes é conhecido como inferência de redes gênicas. Uma das formas para representar o relacionamento entre os genes é usar modelos matemáticos e computacionais de Redes Gênicas. Em especial, um dos modelos de grande interesse é o de Redes Booleanas (BN - do inglês Boolean Networks), no qual os genes podem assumir dois estados, ativo ou inativo, se estão, respectivamente, expressos ou não. Estes estados podem variar ao longo do tempo, dependendo de como os genes se relacionam. Nosso interesse está em estudar um caso particular deste modelo, conhecido como Redes Booleanas Limiarizadas, onde apenas uma classe de funções booleanas é utilizada para construir as BNs. Para inferir as Redes Booleanas Limiarizadas, usamos um algoritmo constituído de dois passos. Primeiro, usamos o arcabouço do Problema de Satisfação de Restrições (CSP - do inglês Constraint Satisfaction Problem) para inferir conjuntos de soluções consistentes com uma dada série temporal de um conjunto de genes. Em seguida analisamos o comportamento dinâmico das soluções encon- tradas , filtrando conjuntos de soluções de maior interesse para testes práticos em laboratório. Usando o arcabouço do CSP, construímos um solver, usando a biblioteca Gecode,1 para inferência de redes consistentes, usando como entrada uma série temporal oriunda de dados de microarrays. Em seguida, através da simulação da dinâmica de uma amostra das redes encontradas no passo anterior, fomos capazes de determinar algumas restrições interessantes para filtrar o conjunto de redes. Aplicamos o nosso método para três conjuntos de dados: dois artificiais, e para validação, usamos uma série temporal de uma rede artificial conhecida na literatura. Com isso fomos capazes de inferir conjuntos de redes gênicas de possível interesse para testes em laboratório. / The chemical reactions that result in gene expression are complex and not yet fully understood. It is known that genes send, receive and process information to form a complex network of com- munication, but the architecture and dynamics of these networks are not fully known. Thus, one major problem is to determine how genes are linked within the cell. This process of determining the relationship between genes is known as inference of genetic networks. One way to represent the relationship between genes is to use mathematical and computer models of genetic networks. In particular, one of the models of great interest are Boolean Networks (BN), in which genes can take two states, active or inactive, if they are, respectively, expressed or not. These states may vary over time, depending on how genes are related. Our interest is in studying a case of this particular model, known as thresholded Boolean networks, where only one class of Boolean functions is used to build the GNs. To infer the thresholded Boolean networks, we use an algorithm that consists of two steps. First, we use the framework of Constraint Satisfaction Problem (CSP) to infer sets of solutions consistent with a time series of a given set of genes. Then analyze the dynamic behavior of the solutions, filtering sets of solutions with interest for practical tests in the laboratory. Using the framework of the CSP, we constructed a solver, using the library Gecode, 2 for in- ference of consistent networks, using as input a time series arising from microarrays data. Then, by simulating the dynamics of a sample of networks found in the previous step, we were able to determine some interesting constraints to filter the set of networks. We apply our method to three datasets: two artificial, and for validation, we use a time series of an artificial network known from literature. Thus we were able to infer genetic networks sets of possible interest for laboratory tests.

The focus of this master's thesis is an automatic creation of organ overtures for church songs from both theoretical and practical points of view. Organ overture is a short introduction to a church song. According to the fact that it can be described by a finite set of rules, it is possible to use techniques for solving Constraint Satisfaction Problems. An effective instrument to develop such system can be C++ programming language and Gecode library.

<p>Doktorska disertacija pripada oblasti<br />informacionih sistema, odnosno podoblasti koja<br />se bavi upravljanjem skladi&scaron;tenjem i<br />pretraživanjem informacija. Osnovni cilj<br />disertacije je modeliranje i implementacija<br />skupa alata koji omogućavaju upotrebu fazi<br />logike u radu sa relacionim bazama podataka.<br />Da bi se do tog skupa alata do&scaron;lo, najpre je<br />relacioni model podataka pro&scaron;iren elementima<br />teorije fazi skupova, a zatim je definisano fazi<br />pro&scaron;irenje upitnog jezika SQL &ndash; PFSQL.<br />Interpreter za taj jezik je implementiran u<br />okviru fazi JDBC drajvera koji, osim<br />implementacije interpretera, sadrži i elemente<br />koji omogućavaju jednostavnu upotrebu ovih<br />mehanizama iz programskog jezika Java. Skup<br />alata je zaokružen implementacijom CASE<br />alata za razvoj fazi-relacionog modela baze<br />podataka. Osim toga, razmatrane su i<br />mogućnosti za upotrebu PFSQL jezika u<br />vi&scaron;eslojnim aplikacijama.</p> / <p>This doctoral dissertation belongs to the<br />field of information systems, subfield<br />information storage and retrieval management.<br />The main subject of the dissertation is modeling<br />and implementation of a set of tools that allow<br />usage of fuzzy logic in relational database<br />applications<br />In order to achieve that goal, at first, the<br />relational data model is extended with elements<br />of fuzzy set theory. After that, a fuzzy<br />extension of the SQL query language, called<br />PFSQL, is defined. An interpreter for that<br />language is implemented as a part of the fuzzy<br />JDBC driver. Beside the implementation of the<br />interpreter, this fuzzy JDBC driver contains<br />elements that allow simple usage of offered<br />mechanisms from Java programming language.<br />The set of tools is concluded with the<br />implementation of the CASE tool for the<br />development of fuzzy-relational data models. In<br />addition, possibilities to use PFSQL language<br />on the middle tier of multi tier systems are<br />discussed.</p>

No description available.

A Probabilistically Checkable Proof (PCP) of a mathematical statement is a proof written in a special manner that allows for efficient probabilistic verification. The celebrated PCP Theorem states that for every family of statements in NP, there is a probabilistic verification procedure that checks the validity of a PCP proof by reading only 3 bits from it. This landmark theorem, and the works leading up to it, laid the foundation for many subsequent works in computational complexity theory, the most prominent among them being the study of inapproximability of combinatorial optimization problems. This thesis focuses on a broad class of combinatorial optimization problems called Constraint Satisfaction Problems (CSPs). In an instance of a CSP problem of arity k, we are given a set of variables taking values from some finite domain, and a set of constraints each involving a subset of at most k variables. The goal is to find an assignment that simultaneously satisfies as many constraints as possible. An alternative formulation of the goal that is commonly used is Gap-CSP, where the goal is to decide whether a CSP instance is satisfiable or far from satisfiable, where the exact meaning of being far from satisfiable varies depending on the problems.We first study Boolean CSPs, where the domain of the variables is {0,1}. The main question we study is the hardness of distinguishing satisfiable Boolean CSP instances from those for which no assignment satisfies more than some epsilon fraction of the constraints. Intuitively, as the arity increases, the CSP gets more complex and thus the hardness parameter epsilon should decrease. We show that for Boolean CSPs of arity k, it is NP-hard to distinguish satisfiable instances from those that are at most 2^{~O(k^{1/3})}/2^k-satisfiable. We also study coloring of graphs and hypergraphs. Given a graph or a hypergraph, a coloring is an assignment of colors to vertices, such that all edges or hyperedges are non-monochromatic. The gap problem is to distinguish instances that are colorable with a small number of colors, from those that require a large number of colors. For graphs, we prove that there exists a constant K_0&gt;0, such that for any K &gt;= K_0, it is NP-hard to distinguish K-colorable graphs from those that require 2^{Omega(K^{1/3})} colors. For hypergraphs, we prove that it is quasi-NP-hard to distinguish 2-colorable 8-uniform hypergraphs of size N from those that require 2^{(log N)^{1/4-o(1)}} colors. In terms of techniques, all these results are based on constructions of PCPs with perfect completeness, that is, PCPs where the probabilistic proof verification procedure always accepts a correct proof. Not only is this a very natural property for proofs, but it can also be an essential requirement in many applications. It has always been particularly challenging to construct PCPs with perfect completeness for NP statements due to limitations in techniques. Our improved hardness results build on and extend many of the current approaches. Our Boolean CSP result and GraphColoring result were proved by adapting the Direct Sum of PCPs idea by Siu On Chan to the perfect completeness setting. Our proof for hypergraph coloring hardness improves and simplifies the recent work by Khot and Saket, in which they proposed the notion of superposition complexity of CSPs. / Ett probabilistiskt verifierbart bevis (eng: Probabilistically Checkable Proof, PCP) av en matematisk sats är ett bevis skrivet på ett speciellt sätt vilket möjliggör en effektiv probabilistisk verifiering. Den berömda PCP-satsen säger att för varje familj av påståenden i NP finns det en probabilistisk verifierare som kontrollerar om en PCP bevis är giltigt genom att läsa endast 3 bitar från det. Denna banbrytande sats, och arbetena som ledde fram till det, lade grunden för många senare arbeten inom komplexitetsteorin, framförallt inom studiet av approximerbarhet av kombinatoriska optimeringsproblem. I denna avhandling fokuserar vi på en bred klass av optimeringsproblem i form av villkorsuppfyllningsproblem (engelska ``Constraint Satisfaction Problems'' CSPs). En instans av ett CSP av aritet k ges av en mängd variabler som tar värden från någon ändlig domän, och ett antal villkor som vart och ett beror på en delmängd av högst k variabler. Målet är att hitta ett tilldelning av variablerna som samtidigt uppfyller så många som möjligt av villkoren. En alternativ formulering av målet som ofta används är Gap-CSP, där målet är att avgöra om en CSP-instans är satisfierbar eller långt ifrån satisfierbar, där den exakta innebörden av att vara ``långt ifrån satisfierbar'' varierar beroende på problemet.Först studerar vi booleska CSPer, där domänen är {0,1}. Den fråga vi studerar är svårigheten av att särskilja satisfierbara boolesk CSP-instanser från instanser där den bästa tilldelningen satisfierar högst en andel epsilon av villkoren. Intuitivt, när ariten ökar blir CSP mer komplexa och därmed bör svårighetsparametern epsilon avta med ökande aritet. Detta visar sig vara sant och ett första resultat är att för booleska CSP av aritet k är det NP-svårt att särskilja satisfierbara instanser från dem som är högst 2^{~O(k^{1/3})}/2^k-satisfierbara. Vidare studerar vi färgläggning av grafer och hypergrafer. Givet en graf eller en hypergraf, är en färgläggning en tilldelning av färger till noderna, så att ingen kant eller hyperkant är monokromatisk. Problemet vi analyserar är att särskilja instanser som är färgbara med ett litet antal färger från dem som behöver många färger. För grafer visar vi att det finns en konstant K_0&gt;0, så att för alla K &gt;= K_0 är det NP-svårt att särskilja grafer som är K-färgbara från dem som kräver minst 2^{Omega(K^{1/3})} färger. För hypergrafer visar vi att det är kvasi-NP-svårt att särskilja 2-färgbara 8-likformiga hypergrafer som har N noder från dem som kräv minst 2^{(log N)^{1/4-o(1)}} färger. Samtliga dessa resultat bygger på konstruktioner av PCPer med perfekt fullständighet. Det vill säga PCPer där verifieraren alltid accepterar ett korrekt bevis. Inte bara är detta en mycket naturlig egenskap för PCPer, men det kan också vara ett nödvändigt krav för vissa tillämpningar. Konstruktionen av PCPer med perfekt fullständighet för NP-påståenden ger tekniska komplikationer och kräver delvis utvecklande av nya metoder. Vårt booleska CSPer resultat och vårt Färgläggning resultat bevisas genom att anpassa ``Direktsumman-metoden'' introducerad av Siu On Chan till fallet med perfekt fullständighet. Vårt bevis för hypergraffärgningssvårighet förbättrar och förenklar ett färskt resultat av Khot och Saket, där de föreslog begreppet superpositionskomplexitet av CSP. / <p>QC 20150916</p>

Les problèmes de satisfaction de contraintes, qui consistent à attribuer des valeurs à des variables en respectant un ensemble de contraintes, constituent une large classe de problèmes naturels. Pour étudier la complexité de ces problèmes, il est commode de les voir comme des problèmes d'homomorphismes vers des structures relationnelles. Un axe de recherche actuel est la caractérisation des classes de complexité auxquelles appartient le problème d'homomorphisme, ceci dans la perspective de confirmer des conjectures reliant les propriétés algébriques des structures relationelles à la complexité du problème d'homomorphisme. Cette thèse propose dans un premier temps la caractérisation des digraphes pour lesquels le problème d'homomorphisme avec listes appartient à FO. On montre également que dans le cas du problèmes d'homomorphisme avec listes sur les digraphes télescopiques, les conjectures reliant algèbre et complexité sont confirmées. Dans un deuxième temps, on caractérise les graphes pour lesquels le problème d'homomorphisme avec listes est résoluble par cohérence d'arc. On introduit la notion de polymorphisme monochromatique et on propose un algorithme simple qui résoud le problème d'homomorphisme avec listes si le graphe cible admet un polymorphisme monochromatique TSI d'arité k pour tout k ≥ 2. / Constraint satisfaction problems, consisting in assigning values to variables while respecting a set of constraints, form a large class of natural problems. In order to study the complexity of these problems, it is convenient to see them as homomorphism problems on relational structures. One current research topic is to characterise complexity classes where the homomorphism problem belongs. The ultimate goal is to confirm conjectures that bind together algebraic properties of the relationnal structure and complexity of the homomorphism problem. At first, the thesis characterizes digraphs which generate FO list-homomorphism problems. It is shown that in the particular case of telescopic digraphs, conjectures binding together algebra and complexity are confirmed. Subsequently, we characterize graphs which generate arc-consistency solvable list-homomorphism problems. We introduce the notion of monochromatic polymorphism and we propose a simple algorithm which solves the list-homomorphism problem if the target graph admits a monochromatic TSI polymorphism of arity k for every k ≥ 2.

La programmation par contraintes intègre des algorithmes de résolution génériques dans des langages de modélisation déclaratifs basés sur les contraintes : ces langages permettent de décrire des problèmes combinatoires sous la forme d’un ensemble de variables devant prendre leurs valeurs dans des domaines en satisfaisant des contraintes. Nous introduisons dans cette thèse un algorithme de résolution générique paramétré par : — une stratégie d’exploration de l’espace de recherche, à choisir parmi, chronological backtracking, conflict directed backjumping, conflict directed backjumping with reordering, dynamic backtracking, decision repair, et backtracking with tree decomposition ; — une heuristique de choix de variables, à choisir parmi, min-domain/ddeg et min-domain/wdeg ; — une technique de propagation de contraintes, à choisir parmi, forward checking et maintaining arc consistency. Ainsi, cet algorithme générique s’instancie en vingt-quatre configurations différentes ; certaines correspondant à des algorithmes connus, d’autres étant nouvelles. Ces vingt- quatre configurations ont été comparées expérimentalement sur un benchmark de plus de mille instances, chaque configuration étant exécutée plusieurs fois sur chaque instance pour tenir compte du non déterminisme des exécutions. Des tests statistiques sont utilisés pour comparer les performances. Cette évaluation expérimentale a permis de mieux comprendre la complémentarité des différents mécanismes de résolution, avec une attention particulière portée sur la capacité à tirer parti de la structure des instances pour accélérer la résolution. Nous identifions notamment treize configurations complémentaires telles que chaque instance de notre benchmark est bien résolue par au moins une des treize configurations. Une deuxième contribution de la thèse est d’introduire un sélecteur capable de choisir automatiquement la meilleure configuration de notre algorithme générique pour chaque nouvelle instance à résoudre : nous décrivons chaque instance par un ensemble de descripteurs et nous utilisons des techniques d’apprentissage automatique pour construire un modèle de choix de configuration à partir de ces descripteurs. Sachant que l’apprentissage est généralement plus difficile quand il y a beaucoup de configurations, nous exprimons le problème du choix du sous-ensemble de configurations pouvant être sélectionnées comme un problème de couverture d’ensemble et nous comparons deux critères de choix : le premier vise à maximiser le nombre d’instances résolues par au moins une configuration et le second vise à maximiser le nombre d’instances pour lesquelles il y a une bonne configuration disponible. Nous montrons expérimentalement que la seconde stratégie obtient généralement de meilleurs résultats, et que le sélecteur obtient de meilleures performances que chacune de nos vingt-quatre configurations initiales. / Constraint programming integrates generic solving algorithms within declarative languages based on constraints : these languages allow us to describe combinatorial problems as a set of variables which have to take their values in domains while satisfying constraints. Numerous real-life problems can be modelled in such a way, as for instance, planification problems, scheduling problems, . . . These problems are NP-complete in the general case of finite domains. We introduce in this work a generic solving algorithm parameterized by : — a strategy for exploring the search space, to be chosen from the following six, chronological backtracking, conflict directed backjumping, conflict directed backjumping with reordering, dynamic backtracking, decision repair, and backtracking with tree decomposition ; — a variable ordering heuristic, to be chosen from the following two, min-domain/ddeg and min-domain/wdeg ; — a constraint propagation technique, to be chosen from the following two, forward checking and maintaining arc consistency. Thus, this algorithm leads to 24 different configurations ; some corresponding to already known algorithms, others being new. These 24 configurations have been com- pared experimentally on a benchmark of more than a thousand instances, each configuration being executed several times to take into account the non-determinism of the executions, and a statistical test has been used to compare performances. This experimental evaluation allowed us to better understand the complementarity of the different solving mechanisms, with a focus on the ability to exploit the structure of the instances to speed up the solving process. We identify 13 complementary configurations such that every instance of our benchmark is well solved by at least one of the 13 configurations. A second contribution of this work is to introduce a selector able to choose automatically the best configuration of our generic solver for each new instance to be solved : we describe each instance by a set of features and we use machine learning techniques to build a model to choose a configuration based on these features. Knowing that the learning process is generally harder when there are many configurations to choose from, we state the problem of choosing a subset of configurations that can be picked as a set covering problem and we compare two criterion : the first one aims to maximize the number of instances solved by at least one configuration and the second one aims to maximize the number of instances for which there is a good configuration available. We show experimentally that the second strategy obtains generally better results and that the selector obtains better performances than each of the 24 initial configurations.

Application of constraint programming (CP) is one of the possible ways of solving discrete problems. It can be used for both search for feasible solution and optimization. CP offers a whole range of approaches for either a solution search or for acceleration of the process of its search -- from search algorithms or consistency techniques to propagation algorithms, which are basically only a combination of the two preceding methods. For optimization we most often use branch and bound approach, which differs in some aspects from a method of the same name used in mathematical programming (MP). Comparison of CP and MP is interesting in many other aspects. With CP the formulation of problems is more flexible, which allows for creation of often simpler and smaller models. On the other hand, its disadvantage is a limited use: Constraint satisfaction (optimisation) problem, as we call the constraint programming problem, cannot contain any discrete variables. CP is suitable especially for problems with a lot of constraints and only few variables, ideally only two. In the beginning, the paper introduces the basic terms of constraint programming, then it describes algorithms and techniques used for solving discrete problems and compares CP with mathematical programming.

Les problèmes de satisfaction de contraintes, qui consistent à attribuer des valeurs à des variables en respectant un ensemble de contraintes, constituent une large classe de problèmes naturels. Pour étudier la complexité de ces problèmes, il est commode de les voir comme des problèmes d'homomorphismes vers des structures relationnelles. Un axe de recherche actuel est la caractérisation des classes de complexité auxquelles appartient le problème d'homomorphisme, ceci dans la perspective de confirmer des conjectures reliant les propriétés algébriques des structures relationelles à la complexité du problème d'homomorphisme. Cette thèse propose dans un premier temps la caractérisation des digraphes pour lesquels le problème d'homomorphisme avec listes appartient à FO. On montre également que dans le cas du problèmes d'homomorphisme avec listes sur les digraphes télescopiques, les conjectures reliant algèbre et complexité sont confirmées. Dans un deuxième temps, on caractérise les graphes pour lesquels le problème d'homomorphisme avec listes est résoluble par cohérence d'arc. On introduit la notion de polymorphisme monochromatique et on propose un algorithme simple qui résoud le problème d'homomorphisme avec listes si le graphe cible admet un polymorphisme monochromatique TSI d'arité k pour tout k ≥ 2. / Constraint satisfaction problems, consisting in assigning values to variables while respecting a set of constraints, form a large class of natural problems. In order to study the complexity of these problems, it is convenient to see them as homomorphism problems on relational structures. One current research topic is to characterise complexity classes where the homomorphism problem belongs. The ultimate goal is to confirm conjectures that bind together algebraic properties of the relationnal structure and complexity of the homomorphism problem. At first, the thesis characterizes digraphs which generate FO list-homomorphism problems. It is shown that in the particular case of telescopic digraphs, conjectures binding together algebra and complexity are confirmed. Subsequently, we characterize graphs which generate arc-consistency solvable list-homomorphism problems. We introduce the notion of monochromatic polymorphism and we propose a simple algorithm which solves the list-homomorphism problem if the target graph admits a monochromatic TSI polymorphism of arity k for every k ≥ 2.

Le processus d'évaluation des risques technologiques, notamment liés au Transport de Matières Dangereuses (TMD), consiste, quand un événement accidentel se produit, à évaluer le niveau de risque potentiel des zones impactées afin de pouvoir dimensionner et prendre rapidement des mesures de prévention et de protection (confinement, évacuation...) dans le but de réduire et maitriser les effets sur les personnes et l'environnement. La première problématique de ce travail consiste donc à évaluer le niveau de risque des zones soumises au transport des matières dangereuses. Pour ce faire, un certain nombre d'informations sont utilisées, comme la quantification de l'intensité des phénomènes qui se produisent à l'aide de modèles d'effets (analytique ou code informatique). Pour ce qui concerne le problème de dispersion de produits toxiques, ces modèles contiennent principalement des variables d'entrée liées à la base de données d'exposition, de données météorologiques,… La deuxième problématique réside dans les incertitudes affectant certaines entrées de ces modèles. Pour correctement réaliser une cartographie en déterminant la zone de de danger où le niveau de risque est jugé trop élevé, il est nécessaire d'identifier et de prendre en compte les incertitudes sur les entrées afin de les propager dans le modèle d'effets et ainsi d'avoir une évaluation fiable du niveau de risque. Une première phase de ce travail a consisté à évaluer et propager l'incertitude sur la concentration qui est induite par les grandeurs d'entrée incertaines lors de son évaluation par les modèles de dispersion. Deux approches sont utilisées pour modéliser et propager les incertitudes : l'approche ensembliste pour les modèles analytiques et l'approche probabiliste (Monte-Carlo) qui est plus classique et utilisable que le modèle de dispersion soit analytique ou défini par du code informatique. L'objectif consiste à comparer les deux approches pour connaitre leurs avantages et inconvénients en termes de précision et temps de calcul afin de résoudre le problème proposé. Pour réaliser les cartographies, deux modèles de dispersion (Gaussien et SLAB) sont utilisés pour évaluer l'intensité des risques dans la zone contaminée. La réalisation des cartographies a été abordée avec une méthode probabiliste (Monte Carlo) qui consiste à inverser le modèle d'effets et avec une méthode ensembliste générique qui consiste à formuler ce problème sous la forme d'un ensemble de contraintes à satisfaire (CSP) et le résoudre ensuite par inversion ensembliste. La deuxième phase a eu pour but d'établir une méthodologie générale pour réaliser les cartographies et améliorer les performances en termes de temps du calcul et de précision. Cette méthodologie s'appuie sur 3 étapes : l'analyse préalable des modèles d'effets utilisés, la proposition d'une nouvelle approche pour la propagation des incertitudes mixant les approches probabiliste et ensembliste en tirant notamment partie des avantages des deux approches précitées, et utilisable pour n'importe quel type de modèle d'effets spatialisé et statique, puis finalement la réalisation des cartographies en inversant les modèles d'effets. L'analyse de sensibilité présente dans la première étape s'adresse classiquement à des modèles probabilistes. Nous discutons de la validité d'utiliser des indices de type Sobol dans le cas de modèles intervalles et nous proposerons un nouvel indice de sensibilité purement intervalle cette fois-ci. / When an accidental event is occurring, the process of technological risk assessment, in particular the one related to Dangerous Goods Transportation (DGT), allows assessing the level of potential risk of impacted areas in order to provide and quickly take prevention and protection actions (containment, evacuation ...). The objective is to reduce and control its effects on people and environment. The first issue of this work is to evaluate the risk level for areas subjected to dangerous goods transportation. The quantification of the intensity of the occurring events needed to do this evaluation is based on effect models (analytical or computer code). Regarding the problem of dispersion of toxic products, these models mainly contain inputs linked to different databases, like the exposure data and meteorological data. The second problematic is related to the uncertainties affecting some model inputs. To determine the geographical danger zone where the estimated risk level is not acceptable, it is necessary to identify and take in consideration the uncertainties on the inputs in aim to propagate them in the effect model and thus to have a reliable evaluation of the risk level. The first phase of this work is to evaluate and propagate the uncertainty on the gas concentration induced by uncertain model inputs during its evaluation by dispersion models. Two approaches are used to model and propagate the uncertainties. The first one is the set-membership approach based on interval calculus for analytical models. The second one is the probabilistic approach (Monte Carlo), which is more classical and used more frequently when the dispersion model is described by an analytic expression or is is defined by a computer code. The objective is to compare the two approaches to define their advantages and disadvantages in terms of precision and computation time to solve the proposed problem. To determine the danger zones, two dispersion models (Gaussian and SLAB) are used to evaluate the risk intensity in the contaminated area. The risk mapping is achieved by using two methods: a probabilistic method (Monte Carlo) which consists in solving an inverse problem on the effect model and a set-membership generic method that defines the problem as a constraint satisfaction problem (CSP) and to resolve it with an set-membership inversion method. The second phase consists in establishing a general methodology to realize the risk mapping and to improve performance in terms of computation time and precision. This methodology is based on three steps: - Firstly the analysis of the used effect model. - Secondly the proposal of a new method for the uncertainty propagationbased on a mix between the probabilistic and set-membership approaches that takes advantage of both approaches and that is suited to any type of spatial and static effect model. -Finally the realization of risk mapping by inversing the effect models. The sensitivity analysis present in the first step is typically addressed to probabilistic models. The validity of using Sobol indices for interval models is discussed and a new interval sensitivity indiceis proposed.