Abstract
End users who need to program within highly interactive direct-
manipulation interfaces should be able to communicate their intentions
through concrete demonstration rather than in terms of symbolic
abstractions. This paper describes a system that learns procedures
in interactive graphics taught to it "by example" by minimally
trained users. It shows how techniques of machine learning and
reactive interfaces can support one another-the former providing
generalization heuristics to identify constraints implicit in user
actions, the latter offering immediate feedback to help the user
clarify hidden constraints and correct errors before they are
planted into the procedure. The teacher's attention is focused on the
learning system's perceptual and inferential shortcomings through a
metaphorical apprentice called Metamouse, which generalizes action
sequences on the fly and eagerly carries out any actions it can
predict. The success of the induction process is assessed quantitatively
by counting erroneous predictions made during example tasks.
Notes
We are currently acquiring citations for the work deposited into this collection. We recognize the distribution rights of this item may have been assigned to another entity, other than the author(s) of the work.If you can provide the citation for this work or you think you own the distribution rights to this work please contact the Institutional Repository Administrator at digitize@ucalgary.ca
