Coglunch - October 27, 2005
"Constraint-Based Learning From Error: Theory and Applications"
Stellan Ohlsson
Psychology, University of Illinois at Chicago
Cognitive systems, whether human or artificial, can learn from the
errors they make when negotiating an unfamiliar task environment. To
benefit from such an error, the learner must detect the error,
attribute it to a particular knowledge structure, and choose among the
many possible revisions of that structure. In this talk, I present a
computational algorithm which addresses these problems in a general
way that relies on reinterpreting declarative knowledge as consisting
of constraints rather than propositions. The resulting theory throws
new light on certain patterns in human learning, and it has generated
a design theory for intelligent tutoring systems. Constraint-based
tutoring systems implemented by Antonija Mitrovic at Canterbury
University demonstrably support human learning, and some have become
viable, Web-accessible products in the commercial market, thus
completing the process of research and development.
Last modified: Fri Feb 10 11:17:44 PST 2006 by emma@csli.stanford.edu