Advertisement

User Modeling in Adaptive Interface

  • Pat Langley
Part of the CISM International Centre for Mechanical Sciences book series (CISM, volume 407)

Abstract

In this paper we examine the notion of adaptive user interfaces, interactive systems that invoke machine learning to improve their interaction with humans. We review some previous work in this emerging area, ranging from software that filters information to systems that support more complex tasks like scheduling. After this, we describe three ongoing research efforts that extend this framework in new directions. Finally, we review previous work that has addressed similar issues and consider some challenges that are presented by the design of adaptive user interfaces.

Keywords

User Model User Preference Interactive Software Case Library Adaptive Interface 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Anderson, J. R. (1984). Cognitive psychology and intelligent tutoring. Proceedings of the Sixth Annual Conference of the Cognitive Science Society, 37–43. Boulder, CO: Lawrence Erlbaum.Google Scholar
  2. Baffes, P. T., and Mooney, R. J. (1995). A novel application of theory refinement to student modeling. Proceedings of the Thirteenth National Conference on Artificial Intelligence, 403–408. Portland, OR: AAAI Press.Google Scholar
  3. Balabanovic, M. (1998). Exploring versus exploiting when learning user models for text recommendation. User Modeling and User-Adapted Interaction 8: 71–102.CrossRefGoogle Scholar
  4. Basu, C., Hirsh, H., and Cohen, W. (1998). Recommendation as classification: Using social and content-based information in recommendation. Proceedings of the Fifteenth National Conference on Artificial Intelligence, 714–720. Madison, WI: AAAI Press.Google Scholar
  5. Billsus, D., and Pazzani, M. (in press). A personal news agent that talks, learns and explains. Proceedings of the Third International Conference on Autonomous Agents. Seattle: ACM Press.Google Scholar
  6. Boone, G. (1998). Concept features in Re:Agent, an intelligent email agent. Proceedings of the Second International Conference on Autonomous Agents, 141–148. Minneapolis, MN: ACM Press.Google Scholar
  7. Brodley, C. E., and Smyth, P. (1997). Applying classification algorithms in practice. Statistics and Computing 7: 45–56.CrossRefGoogle Scholar
  8. Cypher, A. (1991). EAGER: Programming repetitive tasks by example. Proceedings of the Conference on Human Factors in Computing Systems, 33–39. New Orleans: ACM.Google Scholar
  9. Cypher, A. (Ed.). (1993). Watch What I Do: Programming by Demonstration. Cambridge, MA: MIT Press.Google Scholar
  10. Dent, L., Boticario, J., McDermott, J., Mitchell, T., and Zaborowski, D. (1992). A personal learning apprentice. Proceedings of the Tenth National Conference on Artificial Intelligence, 96–103. San Jose, CA: AAAI Press.Google Scholar
  11. Elio, R., and Haddadi, A. (1998). Dialog management for an adaptive database assistant (Technical Report 98–3). Daimler-Benz Research and Technology Center, Palo Alto, CA.Google Scholar
  12. Gervasio, M. T., Iba, W., and Langley, P. (in press). Learning user evaluation functions for adaptive scheduling assistance. Proceedings of the Sixteenth International Conference on Machine Learning. Bled, Slovenia: Morgan Kaufmann.Google Scholar
  13. Hermens, L. A., and Schlimmer, J. C. (1994). A machine-learning apprentice for the completion of repetitive forms. IEEE Expert 9: 28–33.CrossRefGoogle Scholar
  14. Hinkle, D., and Toomey, C. N. (1994). CLAVIER: Applying case-based reasoning to composite part fabrication. Proceedings of the Sixth Innovative Applications of Artificial Intelligence Conference, 55–62. Seattle, WA: AAAI Press.Google Scholar
  15. Lang, K. (1995). NEWSWEEDER: Learning to filter news. Proceedings of the Twelfth International Conference on Machine Learning, 331–339. Lake Tahoe, CA: Morgan Kaufmann.Google Scholar
  16. Langley, P. (1995). Elements of Machine Learning. San Francisco: Morgan Kaufmann.Google Scholar
  17. Langley, P. (1997). Machine learning for adaptive user interfaces. Proceedings of the 21st German Annual Conference on Artificial Intelligence, 53–62. Freiburg, Germany: Springer.Google Scholar
  18. Langley, P., and Ohlsson, S. (1984). Automated cognitive modeling. Proceedings of the Fourth National Conference on Artificial Intelligence, 193–197. Austin, TX: Morgan Kaufmann.Google Scholar
  19. Langley, P., and Simon, H. A. (1995). Applications of machine learning and rule induction. Communications of the ACM 38: 55–64.CrossRefGoogle Scholar
  20. Linden, G., Hanks, S., and Lesh, N. (1997). Interactive assessment of user preference models: The Automated Travel Assistant. Proceedings of the Sixth International Conference on User Modeling, 67–78. Chia Laguna, Sardinia: Springer.Google Scholar
  21. Mitchell, T. M., Mahadevan, S., and Steinberg, L. (1985). Leap: A learning apprentice for VLSI design. Proceedings of the Ninth International Joint Conference on Artificial Intelligence, 573–580. Los Angeles, CA: Morgan Kaufmann.Google Scholar
  22. O’Shea, T. (1979). A self-improving quadratic tutor. International Journal of Man-Machine Studies 11: 97–124.CrossRefGoogle Scholar
  23. Pazzani, M., Muramatsu, J., and Billsus, D. (1996). Syskill & WEBERT: Identifying interesting web sites. Proceedings of the Thirteenth National Conference on Artificial Intelligence, 54–61. Portland, OR: AAAI Press.Google Scholar
  24. Rich, E. (1979). User modeling via stereotypes. Cognitive Science 3: 329–354.CrossRefGoogle Scholar
  25. Rogers, S., Fiechter, C., and Langley, P. (in press). An adaptive interactive agent for route advice. Proceedings of the Third International Conference on Autonomous Agents. Seattle: ACM Press.Google Scholar
  26. Rudström, A. (1995). Applications of machine learning. Licentiate thesis, Department of Computer and Systems Sciences, Stockholm University, Sweden.Google Scholar
  27. Sammut, C., Hurst, S., Kedizer, D., and Michie, D. (1992). Learning to fly. Proceedings of the Ninth International Conference on Machine Learning, 385–393. Aberdeen, Scotland: Morgan Kaufmann.Google Scholar
  28. Schlimmer, J. C., and Hermens, L. A. (1993). Software agents: Completing patterns and constructing user interfaces. Journal of Artificial Intelligence Research 1: 61–89.Google Scholar
  29. Shardanand, U., and Maes, P. (1995). Social information filtering: Algorithms for automating ‘word of mouth’. Proceedings of the Conference on Human Factors in Computing Systems, 210–217. Denver, CO: ACM Press.Google Scholar

Copyright information

© Springer Science+Business Media New York 1999

Authors and Affiliations

  • Pat Langley
    • 1
  1. 1.Adaptive Systems Group DaimlerChrysler Research and Technology CenterPalo AltoUSA

Personalised recommendations