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Personal learning apprentices

  • Tom M. Mitchell
Invited Talks
Part of the Lecture Notes in Computer Science book series (LNCS, volume 542)

Abstract

We believe that learning apprentice systems may make practical a number of personalized work assistant applications which are now impractical due to the prohibitive costs of knowledge base development and maintenance. CAP is a prototype learning apprentice for one such application: personal calendar management. While CAP is still a prototype system with only a fraction of the capabilities we envision, it is already in routine use by one secretary in our environment, collecting training data on a regular basis. Our preliminary learning experiments indicate that CAP's inductive learning methods are sufficient to automatically acquire rules comparable in performance to manually created rules, for the two features of meetings we have tested thus far. We plan to continue development of CAP, and to distribute copies to a number of users in our environment in order to test its ability to customize itself to a variety of individuals.

Keywords

Personal Assistant Prohibitive Cost Collect Training Data Training Meeting Learn Internal Representation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    J. Jourdan, L. Dent, J. McDermott, T. Mitchell, and D. Zabowski. Interfaces that learn: A learning apprentice for calendar management. In S. Minton, editor, Machine Learning Methods for Planning and Scheduling. Morgan-Kaufmann Press, 1991. forthcoming.Google Scholar
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    T.M. Mitchell, S. Mahadevan, and L. Steinberg. Leap: a learning apprentice for vlsi design. In Proceedings of the Ninth International Joint Conference on Artificial Intelligence. Morgan Kaufmann, August 1985.Google Scholar
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    J.R. Quinlan. Induction of decision trees. Machine Learning, 1(1):81–106, 1986.Google Scholar
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    J.R. Quinlan. Generating production rules from decision trees. In Proceedings of the International Joint Conference on Artificial Intelligence. Morgan Kaufmann, August 1987.Google Scholar
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    D.E. Rumelhart, G.E. Hinton, and R.J. Williams. Learning internal representations by error propagation. In D.E. Rumelhart and G.L. and the PDP Research Group McClelland, editors, Parallel Distributed Processing, volume 1, pages 318–362. MIT Press, 1986.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1991

Authors and Affiliations

  • Tom M. Mitchell
    • 1
  1. 1.School of Computer ScienceCarnegie Mellon UniversityPittsburgh

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