Dissolution of Dynamic Electronic Institutions, a first approach: Relevant factors and causes

In the context of the digital business ecosystems, small organizations cooperate between them in order to achieve common goals or offer new services for expanding their markets. There are different approaches for these cooperation models such as virtual enterprises, virtual organizations or dynamic electronic institutions which in their lifecycle have in common a dissolution phase. However this phase has not been studied deeply in the current literature and it lacks formalization. In this paper a first approach for achieving and managing the dissolution phase is proposed, as well as a CBR process in order to support it in a multi-agent system

Disseny de controladors òptims per al robot Pioneer

Aquest projecte titulat: “Disseny de controladors òptims per al robot Pioneer”, té com a funcióincloure en la recerca, que ja està iniciada, del control del Robot Pioneer 2DX, una novaversió d’agents go to per al funcionament del robot.La problemàtica que ens trobem és sobretot per al primer controlador. Fins ara el sistemamulti-agent fet, feia servir un agent go to que generava la trajectòria a seguir i la controlavamitjançant un PID. Introduint un mètode geomètric com és el cas del pure pursuit la cosa escomplica ja que és més complex l’ajustament del funcionament d’aquest. Centrant-nos encanvi el cas del segon controlador el problema es simplifica ja que l’ajustatge d’aquestmateix es pot realitzar de manera empírica i la problemàtica per a la situació en concret esmillora amb major facilitat.És per aquest motiu, sobretot pel primer controlador, que s’han hagut de realitzar algunesmodificacions en el plantejament del projecte al llarg d’aquest. En un principi estava pensatcrear aquest controlador a través de Matlab® mitjançant l’eina Simulink® però perproblemes de software en un moment donat hem hagut de redirigir el projecte cap alllenguatge base de l’estructura multi-agent com és el C++. Per aquest motiu també s’hahagut de prescindir de la implementació d’aquests també en l’estructura LabView®.

Clustering algorithms for anti-money laundering using graph theory and social network analysis

HEMOLIA (a project under European community’s 7th framework programme) is a new generation Anti-Money Laundering (AML) intelligent multi-agent alert and investigation system which in addition to the traditional financial data makes extensive use of modern society’s huge telecom data source, thereby opening up a new dimension of capabilities to all Money Laundering fighters (FIUs, LEAs) and Financial Institutes (Banks, Insurance Companies, etc.). This Master-Thesis project is done at AIA, one of the partners for the HEMOLIA project in Barcelona. The objective of this thesis is to find the clusters in a network drawn by using the financial data. An extensive literature survey has been carried out and several standard algorithms related to networks have been studied and implemented. The clustering problem is a NP-hard problem and several algorithms like K-Means and Hierarchical clustering are being implemented for studying several problems relating to sociology, evolution, anthropology etc. However, these algorithms have certain drawbacks which make them very difficult to implement. The thesis suggests (a) a possible improvement to the K-Means algorithm, (b) a novel approach to the clustering problem using the Genetic Algorithms and (c) a new algorithm for finding the cluster of a node using the Genetic Algorithm.

Adaption of Courses and Learning Environment to the User Context in dotLRN

The explosive growth of Internet during the last years has been reflected in the ever-increasing amount of the diversity and heterogeneity of user preferences, types and features of devices and access networks. Usually the heterogeneity in the context of the users which request Web contents is not taken into account by the servers that deliver them implying that these contents will not always suit their needs. In the particular case of e-learning platforms this issue is especially critical due to the fact that it puts at stake the knowledge acquired by their users. In the following paper we present a system that aims to provide the dotLRN e-learning platform with the capability to adapt to its users context. By integrating dotLRN with a multi-agent hypermedia system, online courses being undertaken by students as well as their learning environment are adapted in real time

Spacial debris collector robot : An AI-based and autonomous debris collector satellite simulation

In this paper the core functions of an artificial intelligence (AI) for controlling a debris collector robot are designed and implemented. Using the robot operating system (ROS) as the base of this work a multi-agent system is built with abilities for task planning.

Analysis, modelling and visualisation of spatiotemporal data for urban studies

The proportion of population living in or around cites is more important than ever. Urban sprawl and car dependence have taken over the pedestrian-friendly compact city. Environmental problems like air pollution, land waste or noise, and health problems are the result of this still continuing process. The urban planners have to find solutions to these complex problems, and at the same time insure the economic performance of the city and its surroundings. At the same time, an increasing quantity of socio-economic and environmental data is acquired. In order to get a better understanding of the processes and phenomena taking place in the complex urban environment, these data should be analysed. Numerous methods for modelling and simulating such a system exist and are still under development and can be exploited by the urban geographers for improving our understanding of the urban metabolism. Modern and innovative visualisation techniques help in communicating the results of such models and simulations. This thesis covers several methods for analysis, modelling, simulation and visualisation of problems related to urban geography. The analysis of high dimensional socio-economic data using artificial neural network techniques, especially self-organising maps, is showed using two examples at different scales. The problem of spatiotemporal modelling and data representation is treated and some possible solutions are shown. The simulation of urban dynamics and more specifically the traffic due to commuting to work is illustrated using multi-agent micro-simulation techniques. A section on visualisation methods presents cartograms for transforming the geographic space into a feature space, and the distance circle map, a centre-based map representation particularly useful for urban agglomerations. Some issues on the importance of scale in urban analysis and clustering of urban phenomena are exposed. A new approach on how to define urban areas at different scales is developed, and the link with percolation theory established. Fractal statistics, especially the lacunarity measure, and scale laws are used for characterising urban clusters. In a last section, the population evolution is modelled using a model close to the well-established gravity model. The work covers quite a wide range of methods useful in urban geography. Methods should still be developed further and at the same time find their way into the daily work and decision process of urban planners. La part de personnes vivant dans une région urbaine est plus élevé que jamais et continue à croître. L'étalement urbain et la dépendance automobile ont supplanté la ville compacte adaptée aux piétons. La pollution de l'air, le gaspillage du sol, le bruit, et des problèmes de santé pour les habitants en sont la conséquence. Les urbanistes doivent trouver, ensemble avec toute la société, des solutions à ces problèmes complexes. En même temps, il faut assurer la performance économique de la ville et de sa région. Actuellement, une quantité grandissante de données socio-économiques et environnementales est récoltée. Pour mieux comprendre les processus et phénomènes du système complexe "ville", ces données doivent être traitées et analysées. Des nombreuses méthodes pour modéliser et simuler un tel système existent et sont continuellement en développement. Elles peuvent être exploitées par le géographe urbain pour améliorer sa connaissance du métabolisme urbain. Des techniques modernes et innovatrices de visualisation aident dans la communication des résultats de tels modèles et simulations. Cette thèse décrit plusieurs méthodes permettant d'analyser, de modéliser, de simuler et de visualiser des phénomènes urbains. L'analyse de données socio-économiques à très haute dimension à l'aide de réseaux de neurones artificiels, notamment des cartes auto-organisatrices, est montré à travers deux exemples aux échelles différentes. Le problème de modélisation spatio-temporelle et de représentation des données est discuté et quelques ébauches de solutions esquissées. La simulation de la dynamique urbaine, et plus spécifiquement du trafic automobile engendré par les pendulaires est illustrée à l'aide d'une simulation multi-agents. Une section sur les méthodes de visualisation montre des cartes en anamorphoses permettant de transformer l'espace géographique en espace fonctionnel. Un autre type de carte, les cartes circulaires, est présenté. Ce type de carte est particulièrement utile pour les agglomérations urbaines. Quelques questions liées à l'importance de l'échelle dans l'analyse urbaine sont également discutées. Une nouvelle approche pour définir des clusters urbains à des échelles différentes est développée, et le lien avec la théorie de la percolation est établi. Des statistiques fractales, notamment la lacunarité, sont utilisées pour caractériser ces clusters urbains. L'évolution de la population est modélisée à l'aide d'un modèle proche du modèle gravitaire bien connu. Le travail couvre une large panoplie de méthodes utiles en géographie urbaine. Toutefois, il est toujours nécessaire de développer plus loin ces méthodes et en même temps, elles doivent trouver leur chemin dans la vie quotidienne des urbanistes et planificateurs.

Sistema de presa de decisions distribuït

DDM is a framework that combines intelligent agents and artificial intelligence traditional algorithms such as classifiers. The central idea of this project is to create a multi-agent system that allows to compare different views into a single one.

Automated monitoring of medical protocols: a secure and distributed architecture

The control of the right application of medical protocols is a key issue in hospital environments. For the automated monitoring of medical protocols, we need a domain-independent language for their representation and a fully, or semi, autonomous system that understands the protocols and supervises their application. In this paper we describe a specification language and a multi-agent system architecture for monitoring medical protocols. We model medical services in hospital environments as specialized domain agents and interpret a medical protocol as a negotiation process between agents. A medical service can be involved in multiple medical protocols, and so specialized domain agents are independent of negotiation processes and autonomous system agents perform monitoring tasks. We present the detailed architecture of the system agents and of an important domain agent, the database broker agent, that is responsible of obtaining relevant information about the clinical history of patients. We also describe how we tackle the problems of privacy, integrity and authentication during the process of exchanging information between agents.

Development of a measuring-control system for energy monitoring in low power

In this thesis a control system for an intelligent low voltage energy grid is presented, focusing on the control system created by using a multi-agent approach which makes it versatile and easy to expand according to the future needs. The control system is capable of forecasting the future energy consumption and decisions making on its own without human interaction when countering problems. The control system is a part of the St. Petersburg State Polytechnic University’s smart grid project that aims to create a smart grid for the university’s own use. The concept of the smart grid is interesting also for the consumers as it brings new possibilities to control own energy consumption and to save money. Smart grids makes it possible to monitor the energy consumption in real-time and to change own habits to save money. The intelligent grid also brings possibilities to integrate the renewable energy sources to the global or the local energy production much better than the current systems. Consumers can also sell their extra power to the global grid if they want.

Genetic programming for the RoboCup Rescue Simulation System

The Robocup Rescue Simulation System (RCRSS) is a dynamic system of multi-agent interaction, simulating a large-scale urban disaster scenario. Teams of rescue agents are charged with the tasks of minimizing civilian casualties and infrastructure damage while competing against limitations on time, communication, and awareness. This thesis provides the first known attempt of applying Genetic Programming (GP) to the development of behaviours necessary to perform well in the RCRSS. Specifically, this thesis studies the suitability of GP to evolve the operational behaviours required of each type of rescue agent in the RCRSS. The system developed is evaluated in terms of the consistency with which expected solutions are the target of convergence as well as by comparison to previous competition results. The results indicate that GP is capable of converging to some forms of expected behaviour, but that additional evolution in strategizing behaviours must be performed in order to become competitive. An enhancement to the standard GP algorithm is proposed which is shown to simplify the initial search space allowing evolution to occur much quicker. In addition, two forms of population are employed and compared in terms of their apparent effects on the evolution of control structures for intelligent rescue agents. The first is a single population in which each individual is comprised of three distinct trees for the respective control of three types of agents, the second is a set of three co-evolving subpopulations one for each type of agent. Multiple populations of cooperating individuals appear to achieve higher proficiencies in training, but testing on unseen instances raises the issue of overfitting.

Processus cérébraux adaptés aux systèmes tutoriels intelligents

Le module de l'apprenant est l'une des composantes les plus importantes d’un Système Tutoriel Intelligent (STI). L'extension du modèle de l'apprenant n'a pas cessé de progresser. Malgré la définition d’un profil cognitif et l’intégration d’un profil émotionnel, le module de l’apprenant demeure non exhaustif. Plusieurs senseurs physiologiques sont utilisés pour raffiner la reconnaissance des états cognitif et émotionnel de l’apprenant mais l’emploi simultané de tous ces senseurs l’encombre. De plus, ils ne sont pas toujours adaptés aux apprenants dont les capacités sont réduites. Par ailleurs, la plupart des stratégies pédagogiques exécutées par le module du tuteur ne sont pas conçues à la base d’une collecte dynamique de données en temps réel, cela diminue donc de leur efficacité. L’objectif de notre recherche est d’explorer l’activité électrique cérébrale et de l’utiliser comme un nouveau canal de communication entre le STI et l’apprenant. Pour ce faire nous proposons de concevoir, d’implémenter et d’évaluer le système multi agents NORA. Grâce aux agents de NORA, il est possible d’interpréter et d’influencer l’activité électrique cérébrale de l’apprenant pour un meilleur apprentissage. Ainsi, NORA enrichit le module apprenant d’un profile cérébral et le module tuteur de quelques nouvelles stratégies neuropédagogiques efficaces. L’intégration de NORA à un STI donne naissance à une nouvelle génération de systèmes tutoriels : les STI Cérébro-sensibles (ou STICS) destinés à aider un plus grand nombre d’apprenants à interagir avec l’ordinateur pour apprendre à gérer leurs émotions, maintenir la concentration et maximiser les conditions favorable à l’apprentissage.

C.A.M.E.L.E.O. : a cultural adaptation methodology for E-learning environment optimization

Mémoire numérisé par la Division de la gestion de documents et des archives de l'Université de Montréal

Applicability of Emergence Engineering to Distributed Systems Scenarios

Genetic Programming can be effectively used to create emergent behavior for a group of autonomous agents. In the process we call Offline Emergence Engineering, the behavior is at first bred in a Genetic Programming environment and then deployed to the agents in the real environment. In this article we shortly describe our approach, introduce an extended behavioral rule syntax, and discuss the impact of the expressiveness of the behavioral description to the generation success, using two scenarios in comparison: the election problem and the distributed critical section problem. We evaluate the results, formulating criteria for the applicability of our approach.

A Language for Interactive Cooperative Agents

Cooperative behaviour of agents within highly dynamic and nondeterministic domains is an active field of research. In particular establishing highly responsive teamwork, where agents are able to react on dynamic changes in the environment while facing unreliable communication and sensory noise, is an open problem. Moreover, modelling such responsive, cooperative behaviour is difficult. In this work, we specify a novel model for cooperative behaviour geared towards highly dynamic domains. In our approach, agents estimate each other’s decision and correct these estimations once they receive contradictory information. We aim at a comprehensive approach for agent teamwork featuring intuitive modelling capabilities for multi-agent activities, abstractions over activities and agents, and a clear operational semantic for the new model. This work encompasses a complete specification of the new language, ALICA.