Abstract
The question of how biological learning and instinctive neural mechanisms interact with each other in the course of development to produce novel, adaptive behaviors was explored via a robotic simulation. Instinctive behavior in the agent was implemented in a hard-wired network which produced obstacle avoidance. Phototactic behavior was produced in two serially connected plastic layers. A self-organizing feature map was combined with a reinforcement learning layer to produce a learning network. The reinforcement came from an internally generated signal. Both the adaptive and fixed networks supplied motor control signals to the robot motors. The sizes of the self-organizing layer, reinforcement layer, and the complexity of the environment were varied and effects on robot phototactic efficiency and accuracy in the mature networks were measured. A significant interaction of the three independent variables was found, supporting the idea that organisms evolve distinct combinations of instinctive and plastic neural mechanisms which are tailored to the demands of the environment in which their species evolved.
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Young, B., Ghirlanda, S., Grasso, F.W. (2012). Parallel Implementation of Instinctual and Learning Neural Mechanisms in a Simulated Mobile Robot. In: Prescott, T.J., Lepora, N.F., Mura, A., Verschure, P.F.M.J. (eds) Biomimetic and Biohybrid Systems. Living Machines 2012. Lecture Notes in Computer Science(), vol 7375. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31525-1_26
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DOI: https://doi.org/10.1007/978-3-642-31525-1_26
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