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Support the Department: Weeg Professorship
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Artificial Intelligence, Cognitive Science, and Explanation-Based Learning
Prof. Gerald DeJong
University of Illinois, Urbana-Champaign
Friday, Jan 29, 2010
4:00-5:00pm, 140 SH (Schaeffer Hall)
Abstract
Artificial intelligence (AI) has always been an exceptionally diverse field.
However, one clear trend has emerged over the last several decades. In
mainstream AI, machine learning has steadily grown in importance. From a
constituent subdivision, co-equal with planning, vision, natural language
processing, robotics, and so on, machine learning has become the engine
underlying these subdivisions and driving many of AI's recent advances. We
will examine this trend as a natural maturing of the discipline.
Concomitantly, mainstream AI has gradually but steadily grown apart from
cognitive science. With technological maturity one expects such departures;
airplanes fly better without flapping their wings. But we will argue that
there is limited justification for this position in AI. By examining recent
cognitive developmental results, we find evidence that even very young
humans employ algorithms that in many respects are quite different from (and
far more advanced) than current machine learning algorithms.
We will examine a new statistical version of explanation-based learning
(EBL) as one direction that may bridge the widening gap between AI and
cognitive science. This approach holds promise for addressing current
issues in machine learning (including relational learning, structural
learning, and feature construction) as well as suggesting new directions for
developmental investigations.
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