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This paper presents a decentralized learning algorithm for learning how to coordinate an automated team of actuated parts designed to build several types of structures specified by a user on a plane surface. The algorithm learns from the environment feedback and agent behavior. This problem is defined as a Markov decision process where agents (actuated parts) are modeled as small cube-shaped robots subject to the Bellman’s equation (Q-learning). The Q-learning algorithm considers the communication and conflict resolution models between the agents that lead to the emergence of intelligent global behavior (in a non-stationary stochastic environment). The main contribution of this paper is to propose a self-assembly approach capable of randomly generating the navigation routes of the multiple agents while learning the structure shape according to the hazardous dispersion area that must be isolated in the environment. Simulation trials show the feasibility of merging between the multi-agent coordination proces...

Convolutional Neural Networks (CNN) have been being widely employed to solve the challenging remote sensing task of aerial scene classification. Nevertheless, it is not straightforward to find single CNN models that can solve all aerial scene classification tasks, allowing the development of a better alternative, which is to fuse CNN-based classifiers into an ensemble. However, an appropriate choice of the classifiers that will belong to the ensemble is a critical factor, as it is unfeasible to employ all the possible classifiers in the literature. Therefore, this work proposes a novel framework based on meta-heuristic optimization for creating optimized ensembles in the context of aerial scene classification. The experimental results were performed across nine meta-heuristic algorithms and three aerial scene literature datasets, being  compared in terms of effectiveness (accuracy), efficiency (execution time), and behavioral performance in different scenarios.  Our results suggest that the Univariate Marg...

Tropical forest conservation is a current issue of social and ecological relevance, due to their important role in the global ecosystem. Tropical forests have a great diversity of fauna and flora, act in the regulation of climate and rainfall, absorb large amounts of carbon dioxide, and serve as a home for countless indigenous peoples. Unfortunately, for years, thousands of hectares have been deforested and degraded in the Amazon rainforest. Government programs and private initiatives have emerged with the aim of mitigating the effects of the caused damage through monitoring and inspection. Most monitoring programs involve the inspection of remote sensing images by specialists, generally counting on the support of computational resources for automatic detection of patterns. This thesis proposes a novel %deforestation detection system called ForestEyes, which aims to detect deforestation in tropical forests based on Citizen Science and Machine Learning approaches. ForestEyes project uses non-specialist volu...