Enhancing the Interaction between Agents and Users

  • Marcelo Armentano
  • Silvia Schiaffino
  • Analía Amandi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5249)

Abstract

A key aspect when interface agents provide personalized assistance to users, is knowing not only a user’s preferences and interests with respect to a software application but also when and how the user prefers to be assisted. To achieve this goal, interface agents have to detect the user’s intention to determine when to assist the user, and the user’s interaction and interruption preferences to provide the right type of assistance at the right time. In this work we describe a user profiling approach that considers these issues within a user profile, which enables the agent to choose the best type of assistance for a given user in a given situation. We also describe the results obtained when evaluating our proposal in a calendar application.

Keywords

intelligent agents user profiling human-computer etiquette 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Agrawal, R., Srikant, R.: Fast Algorithms for Mining Association Rules. In: Proc. 20th Int. Conf. on Very Large Data Bases (VLDB 1994), pp. 487–499 (1994)Google Scholar
  2. 2.
    Charniak, E., Goldman, R.P.: A bayesian model of plan recognition. Artificial Intelligence 64(1), 53–79 (1993)CrossRefGoogle Scholar
  3. 3.
    Fleming, M., Cohen, R.: A user modeling approach to determining system initiative in mixed-initiative AI systems. In: Proc. 18th Int. Conf. On User Modeling, pp. 54–63 (2001)Google Scholar
  4. 4.
    Horvitz, E., Heckerman, D., Hovel, D., Rommelse, K.: The Lumière Project: Bayesian User Modeling For Inferring The Goals And Needs Of Software Users. In: Proc. 14th Conf. on Uncertainty in Artificial Intelligence, pp. 256–265 (1998)Google Scholar
  5. 5.
    Horvitz, E.: Principles of Mixed-Initiative User Interfaces. In: Proc. ACM Conf. Human Factors in Computing Systems (CHI 1999), pp. 159–166 (1999)Google Scholar
  6. 6.
    Jensen, F.: Bayesian Networks and Decision Graphs. Springer, New York (2001)CrossRefMATHGoogle Scholar
  7. 7.
    Klementinen, M., Mannila, H., Ronkainen, P., Toivonen, H., Verkamo, A.I.: Finding interesting rules from large sets of discovered association rules. In: 3rd Int. Conf. on Information and Knowledge Management, pp. 401–407 (1994)Google Scholar
  8. 8.
    Maes, P.: Agents That Reduce Work And Information Overload. Communications of the ACM 37(7), 30–40 (1994)CrossRefGoogle Scholar
  9. 9.
    Miller, C.: Human-computer etiquette: Managing expectations with intentional agents. Communications of the ACM 47(4), 31–34 (2004)CrossRefGoogle Scholar
  10. 10.
    Rich, C., Sidner, C., Leash, N.: Collagen: Applying Collaborative Discourse Theory To Human-Computer Interaction. Artificial Intelligence 22(4), 15–25 (2001)Google Scholar
  11. 11.
    Schiaffino, S., Amandi, A.: User – interface agent interactions: personalization issues. International Journal of Human Computer Studies 60(1), 129–148 (2004)CrossRefGoogle Scholar
  12. 12.
    Schiaffino, S., Amandi, A.: Polite Personal Agents. IEEE Intelligent Systems 21(1), 12–19 (2006)CrossRefGoogle Scholar
  13. 13.
    Shah, D., Lakshmanan, L., Ramamrithnanm, K., Sudarshan, S.: Interestingness and Pruning of Mined Patterns. In: Proc. Workshop Research Issues in Data Mining and Knowledge Discovery. ACM Press, New York (1999)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Marcelo Armentano
    • 1
    • 2
  • Silvia Schiaffino
    • 1
    • 2
  • Analía Amandi
    • 1
    • 2
  1. 1.ISISTAN Research Institute, Fac. Cs. Exactas, UNCPBATandilArgentina
  2. 2.CONICET, Consejo Nacional de Investigaciones Científicas y TécnicasArgentina

Personalised recommendations