Interactive Assessment of User Preference Models: The Automated Travel Assistant
This paper presents the candidate/critique model of interactive problem solving, in which an automated problem solver communicates candidate solutions to the user and the user critiques those solutions. The system starts with minimal information about the user’s preferences, and preferences are elicited and inferred incrementally by analyzing the critiques. The system’s goal is to present “good” candidates to the user, but to do so it must learn as much as possible about his preferences in order to improve its choice of candidates in subsequent iterations. This system contrasts with traditional decision-analytic and planning frameworks in which a complete model is elicited beforehand or is constructed by a human expert. The paper presents the Automated Travel Assistant, an implemented prototype of the model that interactively builds flight itineraries using realtime airline information. The ATA is available on the World Wide Web and has had over 4000 users between May and October 1996.
KeywordsCandidate Solution User Preference Soft Constraint Travel Agent Interactive Assessment
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- Allen, J., et al. (1995). The TRAINS Project: A case study in building a conversational planning agent. Journal of Experimental and Theoretical AI. 7–48.Google Scholar
- Burke, R., Hammond, K., and Young, B. (1996). Knowledge-based navigation of complex information spaces. In Proceedings of the National Conference on Artificial Intelligence (AAAI), 462–468.Google Scholar
- Cleary, D., and Zeleznikow, J. (1991). L-CATA: An intelligent logic based expert travel assistant. In Eleventh International Conference on Expert Systems and their Applications, 111–22.Google Scholar
- Ferguson, G., Allen, J., and Miller, B. (1996). TRAINS-95: Toward a mixed-initiative planning assistant. In Proceedings of the Third Conference on Artificial Intelligence Planning Systems (AIPS), 70–77.Google Scholar
- Karunanithi, N., and Alspector, J. (1996). A feature-based user model for movie selection. In Proceedings of the Fifth International Conference on User Modeling, 29–34.Google Scholar
- Keeney, R., and Raiffa, H. (1976). Decisions With Multiple Objectives: Preferences and Value Tradeoffs. Wiley.Google Scholar
- McCalla, G., Searwar, F., Thomson, J., Collins, J., Sun, Y., and Zhou, B. (1996). Analogical user modelling: A case study in individualized information filtering. In Proceedings of the Fifth International Conference on User Modeling, 13–20.Google Scholar
- Mukhopadhyay, S., Mostafa, J., and Palakal, M. (1996). An adaptive multi-level information filtering system. In Proceedings of the Fifth International Conference on User Modeling, 21–28.Google Scholar
- Raskutti, B., and Zukerman, I. (1993). Generating queries during cooperative consultations. In Proceedings of the 6th Australian Joint Conference on Artificial Intelligence, 389–94.Google Scholar
- Raskutti, B., and Zukerman, I. (1994). Acquisition of information to determine a user’s plan. In Proceedings of the European Conference on Artificial Intelligence, 28–32.Google Scholar
- Siklóssy, L. (1978). Impertinent question-answering systems: Justification and theory. In Proceedings of the ACM National Conference, 39–44.Google Scholar
- Thomas, C., and Fischer, G. (1996). Using agents to improve the usability and usefulness of the World-Wide Web. In Proceedings of the Fifth International Conference on User Modeling, 5–12.Google Scholar