Uma arquitetura baseada em agentes para configuração dinâmica de aplicações inteligentes em ambiente foundation fieldbus

The industrial automation is directly linked to the development of information tecnology. Better hardware solutions, as well as improvements in software development methodologies make possible the rapid growth of the productive process control. In this thesis, we propose an architecture that will allow the joining of two technologies in hardware (industrial network) and software field (multiagent systems). The objective of this proposal is to join those technologies in a multiagent architecture to allow control strategies implementations in to field devices. With this, we intend develop an agents architecture to detect and solve problems which may occur in the industrial network environment. Our work ally machine learning with industrial context, become proposed multiagent architecture adaptable to unfamiliar or unexpected production environment. We used neural networks and presented an allocation strategies of these networks in industrial network field devices. With this we intend to improve decision support at plant level and allow operations human intervention independent

Infra-estrutura para avaliação e testes de protocolos sociais de comunicação em ambientes tridimencionais compartilhados

The advent of the Internet stimulated the appearance of several services. An example is the communication ones present in the users day-by-day. Services as chat and e-mail reach an increasing number of users. This fact is turning the Net a powerful communication medium. The following work explores the use of communication conventional services into the Net infrastructure. We introduce the concept of communication social protocols applied to a shared virtual environment. We argue that communication tools have to be adapted to the Internet potentialities. To do that, we approach some theories of the Communication area and its applicability in a virtual environment context. We define multi-agent architecture to support the offer of these services, as well as, a software and hardware platform to support the accomplishment of experiments using Mixed Reality. Finally, we present the obtained results, experiments and products

Arquitetura multiagente baseada em nuvem de partículas para hibridização de metaheurísticas

This thesis proposes an architecture of a new multiagent system framework for hybridization of metaheuristics inspired on the general Particle Swarm Optimization framework (PSO). The main contribution is to propose an effective approach to solve hard combinatory optimization problems. The choice of PSO as inspiration was given because it is inherently multiagent, allowing explore the features of multiagent systems, such as learning and cooperation techniques. In the proposed architecture, particles are autonomous agents with memory and methods for learning and making decisions, using search strategies to move in the solution space. The concepts of position and velocity originally defined in PSO are redefined for this approach. The proposed architecture was applied to the Traveling Salesman Problem and to the Quadratic Assignment Problem, and computational experiments were performed for testing its effectiveness. The experimental results were promising, with satisfactory performance, whereas the potential of the proposed architecture has not been fully explored. For further researches, the proposed approach will be also applied to multiobjective combinatorial optimization problems, which are closer to real-world problems. In the context of applied research, we intend to work with both students at the undergraduate level and a technical level in the implementation of the proposed architecture in real-world problems

Sistema de agentes poligínicos para esteganálise de imagens digitais

In this work, we propose a multi agent system for digital image steganalysis, based on the poliginic bees model. Such approach aims to solve the problem of automatic steganalysis for digital media, with a case study on digital images. The system architecture was designed not only to detect if a file is suspicious of covering a hidden message, as well to extract the hidden message or information regarding it. Several experiments were performed whose results confirm a substantial enhancement (from 67% to 82% success rate) by using the multi-agent approach, fact not observed in traditional systems. An ongoing application using the technique is the detection of anomalies in digital data produced by sensors that capture brain emissions in little animals. The detection of such anomalies can be used to prove theories and evidences of imagery completion during sleep provided by the brain in visual cortex areas

Uma Análise Comparativa entre Sistemas de Combinação de Classificadores com Distribuição Vertical dos Dados

In systems that combine the outputs of classification methods (combination systems), such as ensembles and multi-agent systems, one of the main constraints is that the base components (classifiers or agents) should be diverse among themselves. In other words, there is clearly no accuracy gain in a system that is composed of a set of identical base components. One way of increasing diversity is through the use of feature selection or data distribution methods in combination systems. In this work, an investigation of the impact of using data distribution methods among the components of combination systems will be performed. In this investigation, different methods of data distribution will be used and an analysis of the combination systems, using several different configurations, will be performed. As a result of this analysis, it is aimed to detect which combination systems are more suitable to use feature distribution among the components