Artificial Intelligence
Learning to Perceive and Act by Trial and Error
Machine Learning
Statistically efficient estimation using population coding
Neural Computation
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Evolutionary Robotics: The Biology,Intelligence,and Technology
Evolutionary Robotics: The Biology,Intelligence,and Technology
Coevolution of active vision and feature selection
Biological Cybernetics
Vision: A Computational Investigation into the Human Representation and Processing of Visual Information
A model of reaching that integrates reinforcement learning and population encoding of postures
SAB'06 Proceedings of the 9th international conference on From Animals to Animats: simulation of Adaptive Behavior
SAB'10 Proceedings of the 11th international conference on Simulation of adaptive behavior: from animals to animats
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The active vision and attention-for-action frameworks propose that in organisms attention and perception are closely integrated with action and learning. This work proposes a novel bio-inspired integrated neural-network architecture that on one side uses attention to guide and furnish the parameters to action, and on the other side uses the effects of action to train the task-oriented top-down attention components of the system. The architecture is tested both with a simulated and a real camera-arm robot engaged in a reaching task. The results highlight the computational opportunities and difficulties deriving from a close integration of attention, action and learning.