Démarche, modèles et outils multi-agents pour l'ingénierie des collectifs cyber-physiques

We call a Collective Cyber-Physical System (CCPS), a system consisting of numerous autonomous execution units achieving tasks of control, communication, data processing or acquisition. These nodes are autonomous in decision making and they can cooperate to overcome gaps of knowledge or individual skills in goal achievement.There are many challenges in the design of these collective systems. This Habilitation thesis discusses various aspects of such a system engineering modeled according to a multi-agent approach.First, a complete CCPS design method is proposed. Its special features are discussed regarding the challenges mentioned above. Agent models and collective models suitable to constrained communications and changing environments are then proposed to facilitate the design of CCPS. Finally, a tool that enables the simulation and the deployment of hw/sw mixed collective systems is presented.These contributions have been used in several academic and industrial projects whose experience feedbacks are discussed.Nous appelons "collectif cyber-physique" un système embarqué en réseau dans lequel les nœuds ont une autonomie de décision et coopèrent spontanément afin de participer à l'accomplissement d'objectifs du système global ou de pallier des manques de connaissances ou de compétences individuelles. Ces objectifs portent notamment sur l'état de leur environnement physique. La conception de ces collectifs présente de nombreux défis. Ce mémoire d'Habilitation propose une discussion des différents aspects de l'ingénierie de ces systèmes que nous modélisons en utilisant le paradigme multi-agent. Tout d'abord, une méthode complète d'analyse et de conception est proposée. Ses différentes particularités sont discutées au regard des différents défis précédemment évoqués. Des modèles d'agent et de collectifs adaptés aux communications contraintes et aux environnements changeants sont alors proposés. Ils permettent de simplifier la conception des collectifs cyber-physiques. Enfin, un outil qui permet la simulation et le déploiement de systèmes collectifs mixtes logiciels/matériels est introduit.Ces contributions ont été éprouvées dans des projets académiques et industriels dont les retours d'expériences sont exploités dans les différentes discussions

Resolução de conflitos em linha : OntoLab uma aplicação ao direito laboral

Dissertação de mestrado em Engenharia de InformáticaA Resolução Alternativa de Conflitos, visa, por um lado, promover o acesso à justiça, sendo um meio alternativo à resolução de conflitos judiciais, neste caso recorre-se aos tribunais arbitrais e julgados de paz. Por outro lado, visa, também, apoiar a criação e o funcionamento de meios extrajudiciais para resolução alternativa de conflitos, incluindo a mediação, negociação e arbitragem. No de Direito, especificamente na área do direito do trabalho, a utilização de métodos de resolução alternativa de conflitos é muito vantajosa. É conhecido o estado actual da justiça em Portugal, grande parte dos casos em litígio arrastam-se ao longo de anos nos tribunais, sem existir previsão de resolução. O recurso à resolução de conflitos em linha, visa retirar dos tribunais vários destes casos, tornando assim a resolução do litígio mais célere, bem como menos dispendiosa para ambas as partes. Estes métodos de resolução online de conflitos são uma abordagem bastante recente, que se serve da Internet e de ferramentas de suporte à decisão. Neste tipo de ambientes as partes interagem e expõe os seus pontos de vista, em qualquer momento e em qualquer local, uma vez que estes sistemas estão disponíveis em linha. Nesta dissertação procurou-se definir qual a melhor metodologia para o desenvolvimento de uma ontologia base, nesta área do conhecimento. Nesse sentido desenvolveu-se uma ontologia e um motor de inferência de conhecimento que actua sobre ela de forma a disponibilizar o conhecimento obtido ao sistema. Com a utilização das ontologias aliadas aos sistemas de resolução de conflitos em linha existiram enormes ganhos para a justiça, na medida em que estes processos tendem a ser mais transparentes, mais rápidos e mais justos.Alternative Dispute Resolution aims to promote access to justice, as an alternative mean to litigation in court. On the other hand, Alternative Dispute Resolution also aims at supporting the establishment and operation of non-judicial means for alternative dispute resolution, including mediation, negotiation and arbitration. In the field of the law, specifically in labor law, Alternative Dispute Resolution is very advantageous. In fact, the current state of the legal field in Portugal is well known, in which in most of the cases a dispute drags on for years in court, without prediction on when to end. The use of alternative methods, aims to remove many of the cases from the courts, thus making the resolution of the dispute faster and less expensive for both parties. These methods of online conflict resolution are a fairly recent approach, which uses Web services and decision support tools. In such environments the parts interact and expose their points of view, at any time and any place, since these systems are available online. In this work we tried to define the best methodology for the development of an ontology base in this area of knowledge. In this sense developed an ontology and an inference engine of knowledge that acts on it in order to provide the knowledge gained to the system. With the combined use of ontologies to online systems of conflict resolution there are huge gains to justice, to the extent that these processes tend to be more transparent, faster and faire

Automating Geospatial RDF Dataset Integration and Enrichment

Over the last years, the Linked Open Data (LOD) has evolved from a mere 12 to more than 10,000 knowledge bases. These knowledge bases come from diverse domains including (but not limited to) publications, life sciences, social networking, government, media, linguistics. Moreover, the LOD cloud also contains a large number of crossdomain knowledge bases such as DBpedia and Yago2. These knowledge bases are commonly managed in a decentralized fashion and contain partly verlapping information. This architectural choice has led to knowledge pertaining to the same domain being published by independent entities in the LOD cloud. For example, information on drugs can be found in Diseasome as well as DBpedia and Drugbank. Furthermore, certain knowledge bases such as DBLP have been published by several bodies, which in turn has lead to duplicated content in the LOD . In addition, large amounts of geo-spatial information have been made available with the growth of heterogeneous Web of Data. The concurrent publication of knowledge bases containing related information promises to become a phenomenon of increasing importance with the growth of the number of independent data providers. Enabling the joint use of the knowledge bases published by these providers for tasks such as federated queries, cross-ontology question answering and data integration is most commonly tackled by creating links between the resources described within these knowledge bases. Within this thesis, we spur the transition from isolated knowledge bases to enriched Linked Data sets where information can be easily integrated and processed. To achieve this goal, we provide concepts, approaches and use cases that facilitate the integration and enrichment of information with other data types that are already present on the Linked Data Web with a focus on geo-spatial data. The first challenge that motivates our work is the lack of measures that use the geographic data for linking geo-spatial knowledge bases. This is partly due to the geo-spatial resources being described by the means of vector geometry. In particular, discrepancies in granularity and error measurements across knowledge bases render the selection of appropriate distance measures for geo-spatial resources difficult. We address this challenge by evaluating existing literature for point set measures that can be used to measure the similarity of vector geometries. Then, we present and evaluate the ten measures that we derived from the literature on samples of three real knowledge bases. The second challenge we address in this thesis is the lack of automatic Link Discovery (LD) approaches capable of dealing with geospatial knowledge bases with missing and erroneous data. To this end, we present Colibri, an unsupervised approach that allows discovering links between knowledge bases while improving the quality of the instance data in these knowledge bases. A Colibri iteration begins by generating links between knowledge bases. Then, the approach makes use of these links to detect resources with probably erroneous or missing information. This erroneous or missing information detected by the approach is finally corrected or added. The third challenge we address is the lack of scalable LD approaches for tackling big geo-spatial knowledge bases. Thus, we present Deterministic Particle-Swarm Optimization (DPSO), a novel load balancing technique for LD on parallel hardware based on particle-swarm optimization. We combine this approach with the Orchid algorithm for geo-spatial linking and evaluate it on real and artificial data sets. The lack of approaches for automatic updating of links of an evolving knowledge base is our fourth challenge. This challenge is addressed in this thesis by the Wombat algorithm. Wombat is a novel approach for the discovery of links between knowledge bases that relies exclusively on positive examples. Wombat is based on generalisation via an upward refinement operator to traverse the space of Link Specifications (LS). We study the theoretical characteristics of Wombat and evaluate it on different benchmark data sets. The last challenge addressed herein is the lack of automatic approaches for geo-spatial knowledge base enrichment. Thus, we propose Deer, a supervised learning approach based on a refinement operator for enriching Resource Description Framework (RDF) data sets. We show how we can use exemplary descriptions of enriched resources to generate accurate enrichment pipelines. We evaluate our approach against manually defined enrichment pipelines and show that our approach can learn accurate pipelines even when provided with a small number of training examples. Each of the proposed approaches is implemented and evaluated against state-of-the-art approaches on real and/or artificial data sets. Moreover, all approaches are peer-reviewed and published in a conference or a journal paper. Throughout this thesis, we detail the ideas, implementation and the evaluation of each of the approaches. Moreover, we discuss each approach and present lessons learned. Finally, we conclude this thesis by presenting a set of possible future extensions and use cases for each of the proposed approaches