Multi Site Coordination using a Multi-Agent System

A new approach of coordination of decisions in a multi site system is proposed. It is based this approach on a multi-agent concept and on the principle of distributed network of enterprises. For this purpose, each enterprise is defined as autonomous and performs simultaneously at the local and global levels. The basic component of our approach is a so-called Virtual Enterprise Node (VEN), where the enterprise network is represented as a set of tiers (like in a product breakdown structure). Within the network, each partner constitutes a VEN, which is in contact with several customers and suppliers. Exchanges between the VENs ensure the autonomy of decision, and guarantiee the consistency of information and material flows. Only two complementary VEN agents are necessary: one for external interactions, the Negotiator Agent (NA) and one for the planning of internal decisions, the Planner Agent (PA). If supply problems occur in the network, two other agents are defined: the Tier Negotiator Agent (TNA) working at the tier level only and the Supply Chain Mediator Agent (SCMA) working at the level of the enterprise network. These two agents are only active when the perturbation occurs. Otherwise, the VENs process the flow of information alone. With this new approach, managing enterprise network becomes much more transparent and looks like managing a simple enterprise in the network. The use of a Multi-Agent System (MAS) allows physical distribution of the decisional system, and procures a heterarchical organization structure with a decentralized control that guaranties the autonomy of each entity and the flexibility of the network

Multi-Armed Bandits for Intelligent Tutoring Systems

We present an approach to Intelligent Tutoring Systems which adaptively personalizes sequences of learning activities to maximize skills acquired by students, taking into account the limited time and motivational resources. At a given point in time, the system proposes to the students the activity which makes them progress faster. We introduce two algorithms that rely on the empirical estimation of the learning progress, RiARiT that uses information about the difficulty of each exercise and ZPDES that uses much less knowledge about the problem. The system is based on the combination of three approaches. First, it leverages recent models of intrinsically motivated learning by transposing them to active teaching, relying on empirical estimation of learning progress provided by specific activities to particular students. Second, it uses state-of-the-art Multi-Arm Bandit (MAB) techniques to efficiently manage the exploration/exploitation challenge of this optimization process. Third, it leverages expert knowledge to constrain and bootstrap initial exploration of the MAB, while requiring only coarse guidance information of the expert and allowing the system to deal with didactic gaps in its knowledge. The system is evaluated in a scenario where 7-8 year old schoolchildren learn how to decompose numbers while manipulating money. Systematic experiments are presented with simulated students, followed by results of a user study across a population of 400 school children

Work Roll Cooling System Design Optimisation in Presence of Uncertainty

Organised by: Cranfield UniversityThe paper presents a framework to optimise the design of work roll based on the cooling performance. The framework develops Meta models from a set of Finite Element Analysis (FEA) of the roll cooling. A design of experiment technique is used to identify the FEA runs. The research also identifies sources of uncertainties in the design process. A robust evolutionary multi-objective algorithm is applied to the design optimisation I order to identify a set of good solutions in the presence of uncertainties both in the decision and objective spaces.Mori Seiki – The Machine Tool Compan

Optimisation des parcours d’apprentissage à l’aide des technologies numériques

Since the "Plan Informatique Pour Tous" in 1985, digital technologies occupy an increasingly importance in education : digital textbooks, dynamic geometry software, learning games, e-learning, blended learning, MOOC, flipped classrooms, educationalrobotics, etc.The aim of our work is to show that some of these technologies can contribute to improve learning, boosting learning contents, emphasizing student motivation by proposing devices suitable for distance learning and personalizing learning paths.The stakes of these issues are important. The need to motivate students and personalize learning is more and more crucial. These are major assets to reduce dropout and promote equal opportunities.Objectives of our work before 2011 : - Gamify contents to make them more motivating.- Visualize concepts by using digital objects. - Virtualize learning objects in order to reduce physical constraints to work methods, to overcome handling difficultiesand disability situations. - Provide tools for interactivity, visualization, computer algebra and geometry for computer environments learning (distance learning platforms, software). - Provide tools for monitoring user activity in order to better track their progress, to follow them with precision, to making them more autonomous.- Experiment with objects both digital and tangible such as robots, to assess their impact in learning. - Build new textbooks by accompanying them with digital devices. This work was continued in recent and more research-driven work.Objectives of our work from 2011 : - Optimize and personalize learning by using artificial intelligence and machine learning algorithms. - Use tangible objects such as robots, that students can manipulate and program, to approach learning differently to provide concrete environment to build new concepts.Depuis le « Plan Informatique Pour Tous » de 1985, les technologies numériques ne cessent d’occuper une place grandissante dans l’enseignement : manuels numériques, logiciels de géométrie dynamique, learning games, e-learning, blended learning, MOOC, classes inversées, robotique éducative, etc.L’ambition de nos travaux est de montrer que certaines de ces technologies peuvent contribuer à améliorer les apprentissages, en dynamisant les contenus, en accentuant la motivation des étudiants, en proposant des dispositifs adaptés à la formation à distance, en personnalisant les parcours pédagogiques. Les enjeux autour de ces questions sont importants. La nécessité de motiver les étudiants et de personnaliser les apprentissages apparaît de plus en plus clairement. Ce sont des atouts majeurs pour lutter contre le décrochage scolaire et pour l’égalité des chances.Objectifs de nos travaux antérieurs à 2011 : — Ludifier et animer des contenus afin de les rendre plus motivants et plus explicites. — Visualiser des concepts en manipulant des objets numériques. — Virtualiser des objets d’apprentissage pour s’affranchir de contraintes matérielles afin de faire travailler des méthodes, de dépasser des difficultés de manipulation et des situations de handicap. — Fournir des outils d’interactivité, de visualisation, de calcul formel et de géométrie pour des environnements informatiques d’apprentissage (plateformes d’enseignement à distance, logiciels).— Fournir des outils de monitoring des activités des utilisateurs afin de suivre au mieux leur progression, afin de pouvoir les suivre au plus près dans leurs cheminements, de leur fournir des retours adaptés et des parcours personnalisés, de les rendre plus autonomes. — Expérimenter des objets à la fois numériques et tangibles tels que les robots pour évaluer leur impact dans les apprentissages.— Repenser les manuels scolaires en les accompagnant de dispositifs numériques.Ces travaux ont trouvé un prolongement ciblé, fortement ancré recherche, dans des travaux plus récents.Objectifs de nos travaux postérieurs à 2011 : — Optimiser et personnaliser en profondeur les apprentissages en faisant appel à l’intelligence artificielle et à des algorithmesde machine learning. — Introduire des objets tangibles, tels que les robots, que les élèves peuvent manipuler, voire programmer, pour éclairer différemment les apprentissages et proposer une approche concrète pour construire de nouveaux concepts

Personnalisation automatique des parcours d’apprentissage dans les Systèmes Tuteurs Intelligents

La recherche d’efficacité des systèmes tutoriels intelligents (STI) est un enjeu majeur. Nous présentons ici une méthode d’optimisation des parcoursd’apprentissage pour chaque apprenant. Nous cherchons à proposer à chaque instant à l’apprenant l’activité qui lui fait faire le plus de progrès dans son apprentissage. Nous introduisons deux algorithmes : RiARiT, qui nécessite des informations préalables sur les activités, et ZPDES, qui n’en a pas besoin

Discussion sur les critères de hiérarchisation des occupations privilégiées en région Centre – Val-de-Loire (fin du 1er Moyen Âge)

International audienceLe Projet Collectif de Recherche « Habitat rural du Moyen Âge en région Centre-Val de Loire » a recensé un peu plus de 200 occupations rurales des VIe-XVe s. étudiés lors des opérations archéologiques de ces 15 dernières années. Entre les établissements ruraux dits « classiques » et les sites aux caractéristiques urbaines, castrales ou défensives évidentes, se place une poignée de sites de la seconde moitié du haut Moyen Âge (IXe-Xe s.) présentant une combinaison de caractères inhabituels sur le plan structurel (cadre de vie, construction) et/ou matériel (consommation, utilisation/fonction).L’examen de ces critères pour sept de ces sites est une occasion pour tenter de questionner globalement cette catégorie de site et vérifier sa pertinence

Improved Performances and Motivation in Intelligent Tutoring Systems: Combining Machine Learning and Learner Choice

Large class sizes pose challenges to personalized learning in schools, which educational technologies, especially intelligent tutoring systems (ITS), aim to address. In this context, the ZPDES algorithm, based on the Learning Progress Hypothesis (LPH) and multi-armed bandit machine learning techniques, sequences exercises that maximize learning progress (LP). This algorithm was previously shown in field studies to boost learning performances for a wider diversity of students compared to a hand-designed curriculum. However, its motivational impact was not assessed. Also, ZPDES did not allow students to express choices. This limitation in agency is at odds with the LPH theory concerned with modeling curiosity-driven learning. We here study how the introduction of such choice possibilities impact both learning efficiency and motivation. The given choice concerns dimensions that are orthogonal to exercise difficulty, acting as a playful feature. In an extensive field study (265 7-8 years old children, RCT design), we compare systems based either on ZPDES or a hand-designed curriculum, both with and without self-choice. We first show that ZPDES improves learning performance and produces a positive and motivating learning experience. We then show that the addition of choice triggers intrinsic motivation and reinforces the learning effectiveness of the LP-based personalization. In doing so, it strengthens the links between intrinsic motivation and performance progress during the serious game. Conversely, deleterious effects of the playful feature are observed for hand-designed linear paths. Thus, the intrinsic motivation elicited by a playful feature is beneficial only if the curriculum personalization is effective for the learner. Such a result deserves great attention due to increased use of playful features in non adaptive educational technologies.Comment: 29 pages, 37 figure

The rising prevalence of prescription opioid injection and its association with hepatitis C incidence among street-drug users

Ce manuscrit est une pré-publication d'un article paru dans Addiction 2012; 107(7): 1318-1327 url: http://www.addictionjournal.org/IRSC et FRSQ - Réseau SIDA et maladies infectieuse