Título: Um Ambiente Evolucionário para Geração de Redes Neurais em Agentes Autônomos
Autores: Roisenberg, Mauro; Barreto, Jorge M.; Azevedo, Fernando M. de
Resumo: Biological inspiration of animal behavior, nervous systems and natural evolution mechanisms, allow the construction of artificial Autonomous Agents (AAs) that, as animals, could work very well in the real world. This paper uses this inspiration to analyze and simulate evolutionary mechanisms capable of creating and developing different neural network topologies with increased complexity. This complexity is related with the behavior repertoire presented by the network, increasing the survival chances of the agents in a given world. Here is described a system capable of simulating different environments where the AAs operates. Through the use of evolutionary programming techniques, the system generates different artificial neural networks that connect the animats’ sensors to its actuators. We introduce and describe a lot of cost functions associated with the neural network complexity, leading to a multi-objective problem, very difficult to solve. Different experiments were made in environments with various levels of complexity. The obtained results confirming the hypothesis in a way that, simpler behaviors can be implemented by feedforward neural networks. As the environment becomes more complex, it is necessary more complex behaviors that only can be implemented by using recurrent neural networks.
Artigo em pdf: 4cbrn_043.pdf
Arquivo BibTex: 4cbrn_043.bib