Intelligence as adaptive behavior: an experiment in computational neuroethology
Intelligence as adaptive behavior: an experiment in computational neuroethology
Toward Spinozist Robotics: Exploring the Minimal Dynamics of Behavioral Preference
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
New models for old questions: evolutionary robotics and the 'A not B' error
ECAL'07 Proceedings of the 9th European conference on Advances in artificial life
The complementary roles of allostatic and contextual control systems in foraging tasks
SAB'10 Proceedings of the 11th international conference on Simulation of adaptive behavior: from animals to animats
Flexible and multistable pattern generation by evolving constrained plastic neurocontrollers
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Ultrastable neuroendocrine robot controller
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
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This study presents an extended model of homeostatic adaptation designed to exploit the internal dynamics of a neural network in the absence of sensory input. In order to avoid typical convergence to asymptotic states under these conditions plastic changes in the network are induced in evolved neurocontrollers leading to a renewal of dynamics that may favour sensorimotor adaptation. Other measures are taken to avoid loss of internal variability (as caused, for instance, by synaptic strength saturation). The method allows the generation of reliable adaptation to morphological disruptions in a simple simulated vehicle using a homeostatic neurocontroller that has been selected to behave homeostatically while performing the desired behaviour but non-homeostatically in other circumstances. The performance is compared with simple homeostatic neural controllers that have only been selected for a positive link between internal and behavioural stability. The extended homeostatic networks perform much better and are more adaptive to morphological disruptions that have never been experienced before by the agents.