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Dale Purves, Byron Boots; Evolution of visually guided behavior in artificial agents. Journal of Vision 2006;6(6):356. doi: https://doi.org/10.1167/6.6.356.
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© ARVO (1962-2015); The Authors (2016-present)
Recent work on brightness, color and form has suggested that human visual percepts represent the probable sources of retinal images rather than stimulus features as such. We have investigated this empirical concept of vision by asking whether agents using neural network control systems evolve successful visually guided behavior based solely on the statistical relationship of images on their sensor arrays and the probable sources of the images in a simulated environment. A virtual environment was created with OpenGL consisting of an arena with a central obstacle, similar to arenas used in evolutionary robotics experiments. The neural control system for each agent comprised a single-layer, feed-forward network that connected all 256 inputs from a sensor array to two output nodes that encoded rotation and translation responses. Each agent's behavioral actions in the environment were evaluated, and the fittest individuals selected to produce a new population according to a standard genetic algorithm. This process was repeated until the average fitness of subsequent generations reached a plateau. Analysis of the actions of evolved agents in response to visual input showed their neural network control systems had incorporated the statistical relationship between projected images and their possible sources, and that this information was used to produce increasingly successful visually guided behavior. The simplicity of this paradigm notwithstanding, these results support the idea that biological vision has evolved to solve the inverse problem on a wholly empirical basis, and provide a novel way of exploring visual processing.
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