Financing public transport services from public funds

The paper deals with the issue of efficiency of public passenger transport through financial support from public funds from the perspective of improving road safety. The aim is to verify the hypothesis that financing public passenger transport from public funds is a significant tool to influence the number of passengers carried by individual automobile transport, and thus it can be used a tool for influencing road safety in a particular territory. The first part of the paper analyses the sources for financial support of public passenger transport. The next part describes the assumptions for improving road safety through increasing the support of public passenger transport. The last part analyses possible impacts of financing public passenger transport on the road safety in relation to the specified hypothesis.Ministry of Education of the Slovak Republic VEGA [1/0143/17 POLIAK

Typicality-based revision for handling exceptions in Description Logics

Abstract. We continue our investigation on how to revise a Description Logic knowledge base when detecting exceptions. Our approach relies on the methodology for debugging a Description Logic terminology, addressing the problem of diagnosing inconsistent ontologies by identifying a minimal subset of axioms responsible for an inconsistency. In the approach we propose, once the source of the inconsistency has been localized, the identified TBox inclusions are revised in order to obtain a consistent knowledge base including the detected exception. We define a revision operator whose aim is to replace inclusions of the form "Cs are Ds" with "typical Cs are Ds", admitting the existence of exceptions, obtaining a knowledge base in the nonmonotonic logic ALC R min T which corresponds to a notion of rational closure for Description Logics of typicality. We also describe an algorithm implementing such a revision operator

Configuration interactive et contraintes : connaissances, filtrage et extensions

The value of our research work is rooted in the following observations :-1- the life cycle of products, systems, services and processes is tending to get shorter ; -2- new designs and updates of products on the market are becoming more and more frequent, leading to increasingly short design cycles ; -3 technologies are constantly changing, requiring permanent, ongoing acquisition of knowledge ; -4-the diversity of products offered on the market is growing all the time, ranging from customizable or configurable to made-to-measure or designed to order.These trends, and the mass of information and knowledge that requires treating as a result of them, are placing heavy demands on designers, requiring ever more attentiveness and increasingly intense cognitive effort. The result is an increased risk that the product does not fully meet the customer’s needs, that it is difficult to implement or manufacture, or that it will be prohibitively expensive. The aim of our work is thus to help the design process to reduce these risks and errors by delivering software tools and methodological environments that serve to capitalize and exploit general, contextual, academic, expert or business knowledge.Our work on various complex industrial cases has led us to take into consideration two kinds of knowledge, involving on the one hand the "product domain" and on the other the "product diversity element". Each kind of knowledge leads to differing industrial cases. The first kind of knowledge encompasses the scientific and technical aspects, but also the specific rules governing the business in question. This knowledge is required in order to define the product itself, and involves issues that can be resolved by aiding the product /system/service design. The second kind of knowledge relates to the diverse nature of the products, and involves issues of customization or configuration of the product/system/service.Our aim is to help in what might be called "routine" design, where different kinds and various types of knowledge exist, due to the recurrent nature of the activity. We consider that aid in design or configuration can be formalized, either completely or partially, in the form of a constraint satisfaction problem (CSP). In this context, we focus more specifically on interactive decision-support, by introducing the principles of filtering or constraint propagation. The diversity of knowledge formalized as a CSP and the interaction with the user allow us to assemble and adapt filtering algorithms in a generic constraint propagation engine, integrated in our CoFiADe software solution.In addition, this formalism based on CSP constraints is complemented by : - ontologies to structure knowledge and facilitate its reuse throughout the development cycle, - analogy-based approaches taking advantage of contextual knowledge encapsulated in the case under study, so as to make recommendations to the user on the choice of values, - evolutionary approaches to optimize the search for multi-criteria solutions.Les travaux de recherche présentés dans ce mémoire trouvent leurs fondements dans les constats suivants :-1- la durée de vie des produits et systèmes tend à se réduire,-2- les conceptions et les actualisations des produits mis sur le marché sont de plus en plus fréquentes alors que les cycles de conception sont toujours plus brefs,-3- les technologies employées en constante évolution nécessitent une acquisition de connaissance permanente,-4- la diversité des produits offerte sur les marchés ne cesse de croître allant des produits personnali- sables ou configurés jusqu’aux produits sur-mesure et conçus à la commande.Ces tendances et la masse d’informations et de connaissances à traiter en découlant exigent des concepteurs toujours plus d’attention et un travail cognitif toujours plus intense. Il en résulte une augmentation des risques, que le produit réponde imparfaitement aux besoins du demandeur, qu’il soit difficilement réalisable et fabricable, ou encore qu’il le soit à un coût prohibitif. L’objectif de nos travaux est donc de limiter ces risques et erreurs en proposant des outils logiciels et des environnements méthodologiques destinés à capitaliser et exploiter des connaissances générales, contextuelles, académiques, expertes ou métier pour aider la conception.Les travaux effectués sur différentes problématiques industrielles ont conduit à prendre en considération deux natures de connaissances relevant du « domaine produit » et de la « diversité produit » conduisant à des problématiques industrielles différentes : la première nature de connaissance recouvre aussi bien des aspects scientifiques et techniques que des règles métier, elle est nécessaire pour la définition du produit et débouche sur des problématiques d’aide à la conception de produit ; la seconde nature est une connaissance liée à la diversité des produits, qui débouche sur les problématiques d’aide à la personnalisation ou configuration de produit.Nous visons à aider un type de conception plutôt « routinier » où de la connaissance de différentes natures et de divers types existe du fait de la récurrence de l’activité. Nous considérons de plus dans nos travaux que l’aide à la conception ou configuration peut se formaliser, complètement ou partiellement, comme un problème de satisfaction de contraintes (CSP). Dans ce cadre, nous nous intéressons plus spécifiquement à l’aide à la décision interactive exploitant les principes de filtrage ou de propagation de contraintes. Notre objectif se décline alors en l’accompagnement des concepteurs dans la construction des solutions répondant au mieux à leurs problèmes, en retirant progressivement de l’espace des solutions, celles qui ne sont plus cohérentes avec les décisions prises, en estimant celles-ci au fil de leur construction et/ou en les optimisant.en complément, nous associons à ce formalisme à base de contraintes CSP :- des ontologies pour structurer les connaissances et faciliter leur réutilisateion sur l’ensemble du cycle de développement,- des approches par analogie exploitant de la connaissance contextuelle encapsulée dans des cas afin de proposer à l’utilisateur des recommandations quant aux choix de valeurs,- des approches évolutionnaires pour optimiser la recherche des solutions de manière multicritère

Qualitative robot planning of object moving by pushing

The thesis belongs to the field of Artificial Intelligence, robotics and qualitative reasoning. The purpose of the work is to use a qualitative simulator for planning qualitative actions of a robot. Our modification of the known QSIM algorithm generates state space, which we search with the heuristic search algorithm A*. Implementations of all algorithms are written in the programming language Prolog. Some machine learning algorithms induce qualitative models using QDE constraints that are not defined in the original QSIM algorithm. One of these QDE constraints is the monotonicity in multiple variables. This QDE constraint was implemented and tested on an artificial domain. Generated robot plans have been tested on an object pushing simulator, which is based on the Box2D engine. For this purpose, an algorithm for plan execution was developed. This plan execution algorithm communicates through an interface, which was also developed as part of the thesis. The interface is responsible for a conversion of numerical data into qualitative states. The interface also implements execution of qualitative actions on the simulator. Plans developed by the proposed algorithm have been tested in two object pushing domains: the case of pushing a vertical cylinder and the case of pushing a block. For this purpose, there was a hand-built qualitative model for each domain. The thesis is concluded with an examination of achieved objectives, a review of potential challenges in the implementation of algorithms and a review of ideas for further research

Découverte de définitions dans le web des données

In this thesis, we are interested in the web of data and knowledge units that can be possibly discovered inside. The web of data can be considered as a very large graph consisting of connected RDF triple databases. An RDF triple, denoted as (subject, predicate, object), represents a relation (i.e. the predicate) existing between two resources (i.e. the subject and the object). Resources can belong to one or more classes, where a class aggregates resources sharing common characteristics. Thus, these RDF triple databases can be seen as interconnected knowledge bases. Most of the time, these knowledge bases are collaboratively built thanks to human users. This is particularly the case of DBpedia, a central knowledge base within the web of data, which encodes Wikipedia content in RDF format. DBpedia is built from two types of Wikipedia data: on the one hand, (semi-)structured data such as infoboxes, and, on the other hand, categories, which are thematic clusters of manually generated pages. However, the semantics of categories in DBpedia, that is, the reason a human agent has bundled resources, is rarely made explicit. In fact, considering a class, a software agent has access to the resources that are regrouped together, i.e. the class extension, but it generally does not have access to the ``reasons'' underlying such a cluster, i.e. it does not have the class intension. Considering a category as a class of resources, we aim at discovering an intensional description of the category. More precisely, given a class extension, we are searching for the related intension. The pair (extension, intension) which is produced provides the final definition and the implementation of classification-based reasoning for software agents. This can be expressed in terms of necessary and sufficient conditions: if x belongs to the class C, then x has the property P (necessary condition), and if x has the property P, then it belongs to the class C (sufficient condition). Two complementary data mining methods allow us to materialize the discovery of definitions, the search for association rules and the search for redescriptions. In this thesis, we first present a state of the art about association rules and redescriptions. Next, we propose an adaptation of each data mining method for the task of definition discovery. Then we detail a set of experiments applied to DBpedia, and we qualitatively and quantitatively compare the two approaches. Finally, we discuss how discovered definitions can be added to DBpedia to improve its quality in terms of consistency and completeness.Dans cette thèse, nous nous intéressons au web des données et aux ``connaissances'' que potentiellement il renferme. Le web des données se présente comme un très grand graphe constitué de bases de triplets RDF connectées entre elles. Un triplet RDF, dénoté (sujet, prédicat, objet), représente une relation (le prédicat) qui existe entre deux ressources (le sujet et l'objet). Les ressources peuvent appartenir à une ou plusieurs classes, où une classe regroupe des ressources partageant des caractéristiques communes. Ainsi, ces bases de triplets RDF peuvent être vues comme des bases de connaissances interconnectées. La plupart du temps ces bases de connaissances sont construites de manière collaborative par des utilisateurs. C'est notamment le cas de DBpedia, une base de connaissances centrale dans le web des données, qui encode le contenu de Wikipédia au format RDF. DBpedia est construite à partir de deux types de données de Wikipédia : d'une part, des données (semi-)structurées telles que les infoboxes et d'autre part les catégories, qui sont des regroupements thématiques de pages générés manuellement. Cependant, la sémantique des catégories dans DBpedia, c'est-à-dire la raison pour laquelle un agent humain a regroupé des ressources, n'est pas explicite. De fait, en considérant une classe, un agent logiciel a accès aux ressources qui y sont regroupées --- il dispose de la définition dite en extension --- mais il n'a généralement pas accès aux ``motifs'' de ce regroupement --- il ne dispose pas de la définition dite en intension. Dans cette thèse, nous cherchons à associer une définition à une catégorie en l'assimilant à une classe de ressources. Plus précisément, nous cherchons à associer une intension à une classe donnée en extension. La paire (extension, intension) produite va fournir la définition recherchée et va autoriser la mise en œuvre d'un raisonnement par classification pour un agent logiciel. Cela peut s'exprimer en termes de conditions nécessaires et suffisantes : si x appartient à la classe C, alors x a la propriété P (condition nécessaire), et si x a la propriété P, alors il appartient à la classe C (condition suffisante). Deux méthodes de fouille de données complémentaires nous permettent de matérialiser la découverte de définitions, la fouille de règles d'association et la fouille de redescriptions. Dans le mémoire, nous présentons d'abord un état de l'art sur les règles d'association et les redescriptions. Ensuite, nous proposons une adaptation de chacune des méthodes pour finaliser la tâche de découverte de définitions. Puis nous détaillons un ensemble d'expérimentations menées sur DBpedia, où nous comparons qualitativement et quantitativement les deux approches. Enfin les définitions découvertes peuvent potentiellement être ajoutées à DBpedia pour améliorer sa qualité en termes de cohérence et de complétud

Healthy snacks consumption and the Theory of Planned Behaviour. The role of anticipated regret

Two empirical studies explored the role of anticipated regret (AR) within the Theory of Planned Behavior (TPB) framework (Ajzen, 1991), applied to the case of healthy snacks consumption. AR captures affective reactions and it can be defined as an unpleasant emotion experienced when people realize or imagine that the present situation would be better if they had made a different decision. In this research AR refers to the expected negative feelings for not having consumed healthy snacks (i.e., inaction regret). The aims were: a) to test whether AR improves the TPB predictive power; b) to analyze whether it acts as moderator within the TPB model relationships. Two longitudinal studies were conducted. Target behaviors were: consumption of fruit and vegetables as snacks (Study 1); consumption of fruit as snacks (Study 2). At time 1, the questionnaire included measures of intention and its antecedents, according to the TPB. Both the affective and evaluative components of attitude were assessed. At time 2, self-reported consumption behaviors were surveyed. Two convenience samples of Italian adults were recruited. In hierarchical regressions, the TPB variables were added at the first step; AR was added at the second step, and the interactions at the last step. Results showed that AR significantly improved the TPB ability to predict both intentions and behaviours, also after controlling for intention. In both studies AR moderated the effect of affective attitude on intention: affective attitude was significant only for people low in AR