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Systems that combine logic programming and statistical inference in theory allow machine learning systems to deal with both relational and statistical information.In practice, however, such applications do not scale very well.The LoSt project was concerned with a compositional approach to overcome those challenges. In particular, we experimented with applying one probabilistic logic programming system, PRISM (Taisuke Sato & Yoshitaka Kameya), based on B-Prolog, to complex, large scale bio-informatical problems.Firstly, some important aspects of the PRISM system and its underlying implementation were optimised for application to large scale data.Secondly, we developed a compositional method of analysis, Bayesian Annotation Networks, where the complex overall task is approximated by identifying and negotiating interdependent constituent subtasks and, in turn, integrating their analytical results according to their interdependencies.Finally, we experimented extensively with the developed framework in the domain of procaryotic gene-finding. As part of the general domain of DNA-annotation, the task of gene-finding is characterized by large sets of extremely long and highly ambiguous sequences of data and, thus, represents a suitably challenging setting for efficient analysis.In general, we concluded that with the computing power of today, probabilistic logic programming systems, as exemplified by PRISM, can be applied efficiently - also in large scale domains. As such, probabilistic logic programming offers extremely expressive models with very clear semantics – facilitating increased focus on domain properties and less on programming complexity.

An active set is a unifying space being able to act as a “bridge” for transferring information, ideas and results between distinct types of uncertainties and different types of applications. An active set is a set of agents who independently deliver true or false values for a given proposition. An active set is not a simple vector of logic values for different propositions, the results are a vector but the set is not.
The difference between an ordinary set and active set is that the ordinary set has passive elements with values of the attributes defined by an external agent, in the active set any element is an agent that internally defines the value of a given attribute for a passive element.
Agents in the active set with a special criteria gives the logic value for the same attribute. So agents in many cases are in a logic conflict and this generate semantic uncertainty on the logic evaluation. Criteria and agents are the two variables by which we give different logic values to the same attribute or proposition. Active sets is beyond the modal logic. In fact given a proposition in modal logic we can evaluate the proposition only when we know the worlds where the proposition is locate. When we evaluate one proposition in one world we cannot evaluate the same proposition in another world. Now in epistemic logic any world is an agent that know that the proposition is true or false. Now the active set is a set of agents as in the epistemic logic but the difference with modal logic is that all the agents (worlds) are not separate but are joined in the evaluation of the given proposition. In active set for one agent and one criteria we have one logic value but for many agents and criteria the evaluation is not true and false but is a matrix of true and false. This matrix  is not only a logic evaluation as in the modal logic but give us the conflicting structure of the active set evaluation. Matrix agent is the vector subspace of the true false agent multi dimension space. Operations among active set include operations in the traditional set , fuzzy sets and rough set as special cases. The agent multi dimensional space to evaluate active set include also the Hilbert multidimensional space where is possible to simulate quantum logic gate. New logic operation are possible as fuzzy gate operations and more complex operations as conflicting solving , consensus operations , syntactic inconsistency , semantic inconsistency and knowledge integration. In the space of the agents evaluations morphotronic geometric operations are the new frontier to model new types of computers , new type of model for wireless communications as cognitive radio. In conclusion Active set open new possibility and new models for the logic.

La classification automatique est devenue  un outil important qui s'est beaucoup développé ces dernières années. Bien que les procédures de classification soient nombreuses et que la majorité d'entre elles ait pour objectif de construire une partition optimale des objets (lignes) ou des variables (colonnes), il existe d'autres méthodes, dites de classification croisée, qui considèrent les deux ensembles simultanément et cherchent à organiser les données en blocs homogènes. Comparées aux méthodes  de classification classiques, les algorithmes de classification croisée ont démontré leur efficacité dans la découverte de structures à partir de matrices de données de grande taille (lignes et/ou colonnes), sparses ou non.  Dans ma présentation, je vais considérer plusieurs approches en insistant particulièrement sur l'approche mélange basée sur les modèles latents par blocs (Latent Block Models) et l'approche factorisation basée sur la tri-factorisation de matrice non négative (Nonnegative Matrix Tri-Factorization).

La recherche en innovation et conception produits s’est progressivement orientée vers l’élaboration d’outils numériques intelligents comme support à l’activité des concepteurs : recherche d’informations adaptée aux métiers de la conception, génération de concepts ou de formes pour le design, indexation et manipulation de données sémantiques liées au modèle produit, middleware intelligent pour la programmation d’outils de conception... Le besoin se fait particulièrement sentir dans les phases amont du processus d’innovation (recueil du besoin, créativité, conception globale) dans lesquelles les données manipulées sont mal définies, ambigües ou floues.

Le rapprochement entre la communauté du génie industriel et celle de l’apprentissage automatique est donc amorcé. Mais il n’est pas sans poser problème. En effet, du coté conception les opportunités permises par ces technologies sont mal maîtrisées, il faut pouvoir traduire les besoins des concepteurs en termes technologiques, tout en maîtrisant la faisabilité. Du coté apprentissage, les besoins des concepteurs ne sont pas directement exploitables pour la recherche et le développement de la technologie, et la mauvaise définition du problème empêche souvent d’identifier les verrous technologiques sous-jacent.

Ainsi donc nous nous intéressons à la question de savoir comment identifier, concevoir et expérimenter des technologies intelligentes pour l’élaboration de systèmes adaptés au besoin des concepteurs ?

Au-delà des concepteurs, cette question de l’adaptation des technologies d’apprentissage au besoin des utilisateurs se trouve aujourd’hui amplifiée par l’émergence de nouveaux terrains applicatifs qui vont nécessiter la coopération entre design produit et intelligence artificielle (internet of things, open data).

Nous attaquerons donc cette problématique au travers de 4 exemples d’élaboration de systèmes support à l’activité de conception, en identifiant les différentes phases critiques par lesquels passe l’élaboration de ces outils intelligents :

-        Recueil et formalisation du besoin : un système d’aide au choix des méthodes de conception de produit [Thèse de Nathalie Lahonde]

-        Traduction du besoin : une expérimentation sur la catégorisation de mots clés issus de verbatims de designers [Projet KENSYS]

-        Conception globale et détaillée :proposition d’un système support à la génération créative d’esquisses pour les designers [Projet ANR GENIUS, www.genius-anr.org]

-        Evaluation : système de support à la recherche d’inspiration [Projet TRENDS, www.trendsproject.org]

Dans le cadre du projet Européen Symbrion, nous étudions la possibilité de concevoir des essaims de robots capables de survivre dans des environnements inconnus, et potentiellement changeants. Dans cet exposé, je présenterai nos travaux récents sur la conception d'algorithmes distribués pour garantir l'auto-adaptation en ligne d'un essaim de robots, et qui s'intègre dans le cadre des algorithmes dits d'évolution artificielle. L'évolution artificielle permet en effet d'aborder des problèmes pour lesquels la tâche est définie de manière incomplète ou partielle, et où l'espace de recherche entretient peu de relation avec l'espace des solutions. Je présenterai en particulier les résultats d'une expérience menée avec une vingtaine de robots réels, ainsi que plusieurs travaux récents mettant en jeu l'interaction entre un environnement contraint et le processus d'auto-adaptation.

Après avoir précisé les modalités de réaction des prix boursiers aux annonces financières en présence d’incertitude, nous présenterons les principaux défis empiriques posés par l’étude du rôle des médias sur les marchés boursiers : comment distinguer, notamment, le contenu informationnel des annonces du contexte informationnel des marchés.
La présentation s’appuiera sur le cas particulier de la réponse des marchés et des média aux accidents industriels.

We are witnessing the resurgence of analytics as a key differentiator for creating new services, the emergence of cloud computing as a disrupting technology for service delivery, and the growth of crowdsourcing as a new phenomenon in which people play critical roles in creating information and shaping decisions in a variety of problems. After introducing the first two (well-known) concepts, we will analyze some of the opportunities created by the advent of crowdsourcing. Then, we will explore the intersections of these three concepts.  We will examine their evolution from the optics of a professional machine-learning researcher and try to understand how his job and roles have evolved over time. In the past, analytic model building was an artisanal process, as models were handcrafted by an experienced, knowledgeable model-builder. More recently, the use of meta-heuristics (such as evolutionary algorithms) has provided us with limited levels of automation in model building and maintenance.  In the not so distant future, we expect analytic models to become a commodity. We envision having access to a large number of data-driven models, obtained by a combination of crowdsourcing, crowdservicing, cloud-based evolutionary algorithms, outsourcing, in-house development, and legacy models. In this new context, the critical issue will be model ensemble selection and fusion, rather than model generation. We address this issue by proposing customized model ensembles on demand, inspired by Lazy Learning. In our approach, referred to as Lazy Meta-Learning, for a given query we find the most relevant models from a DB of models, using their meta-information. After retrieving the relevant models, we select a subset of models with highly uncorrelated errors (unless diversity was injected in their design process.)  With these models we create an ensemble and use their meta-information for dynamic bias compensation and relevance weighting. The output is a weighted interpolation or extrapolation of the outputs of the models ensemble. The confidence interval around the output is reduced as we increase the number of uncorrelated models in the ensemble. This approach is agnostic with respect to the genesis of the models, making it scalable and suitable for a variety of applications.  We have successfully tested this approach in a regression problem for a power plant management application, using two different sources of models: bootstrapped neural networks, and GP-created symbolic regressors evolved on a cloud.

In the field of complex event processing, among others, there is a need for computational frameworks supporting real-time reasoning, reasoning under uncertainty and automated knowledge construction. I will present a dialect of the Event Calculus that meets these requirements. The Event Calculus is a logic programming language for representing and reasoning about events and their effects. Our dialect includes novel caching techniques that allow for efficient temporal reasoning, scalable to large data streams. Furthermore, when ported to probabilistic frameworks, it may support various types of uncertainty, such noisy data streams and imprecise knowledge. To avoid the time-consuming, error-prone process of manual knowledge construction, I will present techniques for incremental structure learning that take advantage of large datasets. The Event Calculus dialect will be illustrated with the use of two real-world applications: complex event recognition for city transport management and public space surveillance.

Dans cet exposé, nous discuterons de l'intérêt de la fouille exploratoire de données pour tenter de découvrir des structures dans des espaces de recherche associés à des problèmes d'optimisation combinatoire, et ainsi améliorer les performances expérimentales de certaines méta-heuristiques. Des illustrations seront données notamment sur le problème de coloration de graphes.