- 1.Cypher, A. EAGER: Programming Repetitive Tasks by Example. In Proceedings of CttI, 1991 (New Orleans, Louisiana, April 28 - May 2). ACM, New York, 1991, pp. 33-39. Google ScholarDigital Library
- 2.Dent L., Boticario J., McDermott J., Mitchell T. and Zabowski D. A Personal Learning Apprentice. Submitted to the 1992 National Conference on Artificial Intelligence. 1992.Google Scholar
- 3.Fawcett, T., and Utgoff, P. Automatic Feature Generation for Problem Solving Systems, (COINS Technical Report 92-9). University of Massachusetts, Department of Computer and Information Science, Amherst, MA, 1992. Google ScholarDigital Library
- 4.Kay A. Computer Software. Scientific American 251, 3 (March 1984).Google Scholar
- 5.Laurel B. Interface Agents: Metaphors with Character. In: B. Laurel (ed), The Art of Human- Computer Interface Design. Addison-Wesley, 1990.Google Scholar
- 6.Lieberman, H. Capturing Graphical Expertise Interactively by Example. To be published in Proceedings of the International Center for Scientific and Technical Information (Moscow) Workshop on Human-Computer Interaction (St. Petersburg, Russia, August 1992).Google Scholar
- 7.Maes, P. and Kozierok, R. Learning Interface Agents. Submitted to INTERCHI'93 (Amsterdam, The Netherlands, April 25-29) ACM, 1993.Google Scholar
- 8.Myers, B. and Buxton, W. Creating Highly Interactive and Graphical User Interfaces by Demonstration. In Proceedings of SIGGRAPH 1986, Vol. 20, No. 4. (Dallas, TX, Aug. 18-22) ACM, 1986. Google ScholarDigital Library
- 9.Myers, B. Creating User Interfaces by Demonstrations. Academic Press, 1988. Google ScholarDigital Library
- 10.Stanfill C. and Waltz D. Toward Memory-Based Reasoning. Communications of the ACM 29, 12 (Dec. 1986), 1213-1228. Google ScholarDigital Library
Index Terms
- A learning interface agent for scheduling meetings
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