{"pk":32064,"title":"Bottom-up Skill Learning in Reactive Sequential Decision Tasks","subtitle":null,"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.","language":"eng","license":{"name":"","short_name":"","text":null,"url":""},"keywords":[],"section":"Posters","is_remote":true,"remote_url":"https://escholarship.org/uc/item/5d59m60n","frozenauthors":[{"first_name":"Ron","middle_name":"","last_name":"Sun","name_suffix":"","institution":"The University of Alabama","department":""},{"first_name":"Todd","middle_name":"","last_name":"Peterson","name_suffix":"","institution":"The University of Alabama","department":""},{"first_name":"Edward","middle_name":"","last_name":"Merrill","name_suffix":"","institution":"The University of Alabama","department":""}],"date_submitted":null,"date_accepted":null,"date_published":"1996-01-01T18:00:00Z","render_galley":null,"galleys":[{"label":"PDF","type":"pdf","path":"https://journalpub.escholarship.org/cognitivesciencesociety/article/32064/galley/23129/download/"}]}