Sobrevivência e tamanho populacional do Tiê-preto Tachyphonus Rufus (aves: thraupidae) em fragmento de restinga no extremo Norte da Mata Atlântica

For a long time, we believed in the pattern that tropical and south hemisphere species have high survival. Nowadays results began to contradict this pattern, indicating the need for further studies. Despite the advanced state of the study of bird population parameters, little is known about their variation throughout the year and the factors affecting them. Reproduction, for example, is one factor that may alter adult survival rates, because during this process the breeding pair allocates resources to maintain itself to maintain offspring, making itself more susceptible to diseases and predation. The aim of this study was to estimate survival and population size of a Central and South America passerine, Tachyphonus rufus (Boddaert, 1783), testing hypotheses about the factors that define these parameters. We performed data collection between Nov/2010 and ago/2012 in 12 ha plot, in a fragment of Atlantic Forest in northeastern Brazil. We used capture-mark-recapture methods to generate estimates using Closed Design Robust model in the program MARK. We generated Multi-state models to test some assumptions inherent to Closed Robust Design. The influence of co-variables (time, rain and reproductive cycle) and the effect of transient individuals were measured. Capture, recapture and apparent survival parameters were defined by reproductive cycle, while temporary dispersal was influence by rain. The estimates showed a higher apparent survival during the non-breeding period (92% ± 1%) than during breeding (40% ± 9%), revealing a cost of reproduction and suggesting a trade-off between surviving and reproducing. The low annual survival observed (34%) did not corroborate the pattern of high rates expected for a tropical bird. The largest population size was estimated to be 56 individuals in Nov/11, explained by high recruitment of juveniles, while the lowest observed in May/12: 10 individuals, probably as a result of massive influx of competitor species. Results from this study add to the growing literature on life history of Neotropical species. We encourage studies like this especially in Brazil, where there are few information, and suggest that covariates related to habitat quality and environmental changes should be tested, so that we can generate increasingly reliable models

Análise e desenvolvimento de controladores preditivos multivariáveis baseados em multi-modelos bilineares

This work addresses issues related to analysis and development of multivariable predictive controllers based on bilinear multi-models. Linear Generalized Predictive Control (GPC) monovariable and multivariable is shown, and highlighted its properties, key features and applications in industry. Bilinear GPC, the basis for the development of this thesis, is presented by the time-step quasilinearization approach. Some results are presented using this controller in order to show its best performance when compared to linear GPC, since the bilinear models represent better the dynamics of certain processes. Time-step quasilinearization, due to the fact that it is an approximation, causes a prediction error, which limits the performance of this controller when prediction horizon increases. Due to its prediction error, Bilinear GPC with iterative compensation is shown in order to minimize this error, seeking a better performance than the classic Bilinear GPC. Results of iterative compensation algorithm are shown. The use of multi-model is discussed in this thesis, in order to correct the deficiency of controllers based on single model, when they are applied in cases with large operation ranges. Methods of measuring the distance between models, also called metrics, are the main contribution of this thesis. Several application results in simulated distillation columns, which are close enough to actual behaviour of them, are made, and the results have shown satisfactory

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