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Investigator(s): Dan Ventura ventura@cs.byu.edu(Principal Investigator) Sponsor: Brigham Young University Provo, UT 84602 ABSTRACT One of the powerful aspects of inductive learning is the ability for models to generalize – to perform well on data upon which they were not trained. Unfortunately, this ability has so far been exhibited in only a very limited fashion in sub-symbolic artificial systems, almost exclusively as generalization across instances of a single task. One of the limitations of today’s “intelligent” systems is a fragility due to an inability to generalize across tasks. Learning transfer is the ability for a system to learn one problem and then to transfer a significant amount of the learned knowledge to a different problem, allowing instant performance gains and significantly reducing the learning necessary to become proficient on the second problem. Creativity is required in deciding which prior knowledge to use and how to use it. Symbolic systems employing some form of analogy for learning transfer have been somewhat successful here; however, these approaches require a significant amount of specialized domain knowledge and do not generalize. We propose the use of sub-symbolic approaches to creative problem solving (via learning transfer), trading the interpretability of symbolic approaches for representational power and generality. Last modified 6 January 2009 at 4:37 pm by DanVentura |