{"pk":32943,"title":"Perception-mediated Learning and Reasoning in the CHILDLIKE System","subtitle":null,"abstract":"Intelligent agents interacting with their environments combine information from several sense modalities and indulge in tasks that have components of perception, reasoning, learning and planning. Traditional AI systems focus on a single component. This paper highlights the importance of the integrated perceive-reason-act-learn loop, and describes a system designed to capture this loop. As a first step, it learns about simple objects, their qualities, and the words that name and describe them. The visual-linguistic associations formed serve as a bias in acquiring further knowledge about actions, which in turn aids the system in satisfying its internal needs (e.g., hunger, thirst, sleep, curiosity). Learning mechanisms that extract, aggregate, generate, de-generate and generalize build a hierarchical network (that serves as internal models of the environment) with which the system perceives and reasons.","language":"eng","license":{"name":"","short_name":"","text":null,"url":""},"keywords":[],"section":"Poster Presentations","is_remote":true,"remote_url":"https://escholarship.org/uc/item/20f5r9f5","frozenauthors":[{"first_name":"Ganesh","middle_name":"","last_name":"Mani","name_suffix":"","institution":"Universtiy of Wisconsin, Madison","department":""},{"first_name":"Leonard","middle_name":"","last_name":"Uhr","name_suffix":"","institution":"Universtiy of Wisconsin, Madison","department":""}],"date_submitted":null,"date_accepted":null,"date_published":"1991-01-01T18:00:00Z","render_galley":null,"galleys":[{"label":"PDF","type":"pdf","path":"https://journalpub.escholarship.org/cognitivesciencesociety/article/32943/galley/24003/download/"}]}