Um ambiente multi-middleware para desenvolvimento de aplicações distribuídas

This work presents a proposal of a multi-middleware environment to develop distributed applications, which abstracts different underlying middleware platforms. This work describes: (i) the reference architecture designed for the environment, (ii) an implementation which aims to validate the specified architecture integrating CORBA and EJB, (iii) a case study illustrating the use of the environment, (iv) a performance analysis. The proposed environment allows interoperability on middleware platforms, allowing the reuse of components of different kinds of middleware platforms in a transparency away to the developer and without major losses in performance. Also in the implementation we developed an Eclipse plugin which allows developers gain greater productivity at developing distributed applications using the proposed environment

Participatory design in the project of virtual learning environment of histology

This present article describes a research on the development, under the approach of participatory design, a virtual teaching-learning of Histology in which students and teachers participated actively in all stages of development of the educational environment. We postulates that the development of virtual learning environment of Histology, through the Participatory Design approach, contributes to greater acceptance and use by students and that the adoption of virtual environment for teaching and learning by teachers is a determining factor of use by students

Pose estimation of a humanoid robot using images from a mobile external camera

NOGUEIRA, Marcelo B. ; MEDEIROS, Adelardo A. D. ; ALSINA, Pablo J. Pose Estimation of a Humanoid Robot Using Images from an Mobile Extern Camera. In: IFAC WORKSHOP ON MULTIVEHICLE SYSTEMS, 2006, Salvador, BA. Anais... Salvador: MVS 2006, 2006.

Acessibilidade no ambiente escolar: reflexões com base no estudo de duas escolas municipais de Natal-RN

This work was originated through the results of the analysis of the services for the needs of people with disabilities that were permitted by the physical space of two schools of the municipality of Natal/RN. The general objective/goal was to subsidize the elaboration of alternatives for the planning of environments that could be used by any person. The study used the empirical research through the adoption of a multimethod approach including: (i) technical visits oriented by the NBR 9050, (ii) contact with users that have reduced mobility (visually impaired and wheelchair or crutch users) through escorted travels and interviews, and (iii) interview with school managers. The evidence from the research, even though with significant development of laws that guarantee people with disabilities their right to citizenship, the physical environment of our schools still present with many obstacles that prevent the mobility of people with disabilities which proves their lack of readiness to accommodate them. Therefore, the actions taken to address the accessibility has been the adoption of temporary solutions that makes the adaptation more difficult, adds obstacles and reinforces the undesirable segregation, however still very present in our society. Finally, there is the indication that in order to achieve the spatial configuration that promotes social contact and integration in between the persons with different physical status, it is necessary to completely comprehend the activities developed in each space, from the conception of the equipment to the individual learning needs, having in mind creating environments that stimulates the execution of the tasks in an independent manner without the assistance of others. The inclusion regarding attention to accessibility in the decision making process, directed to the arquitectural and urban project, would decrease the constant need to redevelop and adapt spaces, and should be definitely incorporated as an important component in the production of space

Aprendizado por Reforço com Valores deInfluência em Sistemas Multi-Agente

We propose a new paradigm for collective learning in multi-agent systems (MAS) as a solution to the problem in which several agents acting over the same environment must learn how to perform tasks, simultaneously, based on feedbacks given by each one of the other agents. We introduce the proposed paradigm in the form of a reinforcement learning algorithm, nominating it as reinforcement learning with influence values. While learning by rewards, each agent evaluates the relation between the current state and/or action executed at this state (actual believe) together with the reward obtained after all agents that are interacting perform their actions. The reward is a result of the interference of others. The agent considers the opinions of all its colleagues in order to attempt to change the values of its states and/or actions. The idea is that the system, as a whole, must reach an equilibrium, where all agents get satisfied with the obtained results. This means that the values of the state/actions pairs match the reward obtained by each agent. This dynamical way of setting the values for states and/or actions makes this new reinforcement learning paradigm the first to include, naturally, the fact that the presence of other agents in the environment turns it a dynamical model. As a direct result, we implicitly include the internal state, the actions and the rewards obtained by all the other agents in the internal state of each agent. This makes our proposal the first complete solution to the conceptual problem that rises when applying reinforcement learning in multi-agent systems, which is caused by the difference existent between the environment and agent models. With basis on the proposed model, we create the IVQ-learning algorithm that is exhaustive tested in repetitive games with two, three and four agents and in stochastic games that need cooperation and in games that need collaboration. This algorithm shows to be a good option for obtaining solutions that guarantee convergence to the Nash optimum equilibrium in cooperative problems. Experiments performed clear shows that the proposed paradigm is theoretical and experimentally superior to the traditional approaches. Yet, with the creation of this new paradigm the set of reinforcement learning applications in MAS grows up. That is, besides the possibility of applying the algorithm in traditional learning problems in MAS, as for example coordination of tasks in multi-robot systems, it is possible to apply reinforcement learning in problems that are essentially collaborative