Bottom-up skill learning in reactive sequential decision tasks
| Abstract | This paper introduces a hybrid model that unifies connectionist, symbolic, and reinforcement learning into an integrated architecture for bottom-up skill learning in reactive sequential decision tasks. The model is designed for an agent to learn continuously from on-going experience in the world, without the use of preconceived concepts and knowledge. Both procedural skills and high-level knowledge are acquired through an agent’s experience interacting with the world. Computational experiments with the model in two domains are reported. | |||||||||
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Similar books and articlesDaniel B. Willingham, Joanna Salidis & John D. E. Gabrieli (2002). Direct Comparison of Neural Systems Mediating Conscious and Unconscious Skill Learning. Journal of Neurophysiology 88 (3):1451-1460.
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