ABSTRACT
In many practical scenarios, users are faced with the problem of choosing the most preferred outcome from a large set of possibilities. As people are unable to sift through them manually, decisions support systems are often used to automatically find the optimal solution. A crucial requirement for such a system is to have an accurate model of the user's preferences.Studies have shown that people are usually unable to accurately state their preferences up front, but are greatly helped by seeing examples of actual solutions. Thus, several researchers have proposed preference elicitation strategies based on example critiquing. The essential design question in example critiquing is what examples to show users in order to best help them locate their most preferred solution.In this paper, we analyze this question based on two requirements. The first is that it must stimulate the user to express further preferences by showing the range of alternatives available. The second is that the examples that are shown must contain the solution that the user would consider optimal if the currently expressed preference model was complete so that he select it as a final solution.
- C. Boutilier, R. Brafman, C. Geib, and D. Poole: "A Constraint-Based Approach to Preference Elicitation and Decision Making," AAAI Spring Symposium on Qualitative Decision Theory, Stanford, 1997.Google Scholar
- R. Burke, K. Hammond, and B. Young: "The findme approach to assisted browsing," IEEE Expert 12(4), pp. 32--40, 1997. Google ScholarDigital Library
- G. Carenini and D. Poole: "Constructed Preferences and Value-focused Thinking: Implications for AI research on Preference Elicitation," AAAI-02 Workshop on Preferences in AI and CP: symbolic approaches - Edmonton, Canada, 2002.Google Scholar
- J. Doyle and R. Thomason: "Background to qualitative decision theory," AI Magazine 20(2), Summer 1999.Google Scholar
- B. Faltings, M. Torrens and P. Pu: "Solution generation with qualitative models of preferences," to appear in Computational Intelligence, special issue on qualitative decision theory, 2004, available at http:\\liawww.epfl.ch\~ faltings\compint-2004.pdf.Google Scholar
- V. Ha and P. Haddawy: "Problem-focused incremental elicitation of multiattribute utility models," 13th Conference on Uncertainty in Artificial Intelligence, pp. 215--222, August 1997. Google ScholarDigital Library
- V. Ha and P. Haddawy: "Toward Case-Based Preference Elicitation: Similarity Measures on Preference Structures," 14th Conference on Uncertainty in Artificial Intelligence. 1998. Google ScholarDigital Library
- R.Keeney and H. Raiffa: "Decision with multiple objectives: Preferences and value tradeoffs," Cambridge University Press, 1976.Google Scholar
- R. Keeney: "Value-Focused Thinking: A Path to Creative Decision Making," Harvard University Press, 1992.Google Scholar
- G. Linden, S. Hanks, and N. Lesh: "Interactive assessment of user preference models: The automated travel assistant," In Proceedings of User Modeling '97, 1997.Google Scholar
- J.W. Payne, J.R. Bettman and E.J. Johnson: "The Adaptive Decision Maker," Cambridge University Press, 1993.Google Scholar
- P. Pu and B. Faltings: "Enriching Buyers' experiences: the SmartClient Approach," ACM SIGCHI 2000 Conference on Human Factors in Computing Systems, pp. 289--296, 2000. Google ScholarDigital Library
- S. Shearin and H. Lieberman: "Intelligent Profiling by Example," International Conference on Intelligent User Interfaces (IUI 2001), pp. 145--152, 2001. Google ScholarDigital Library
- M. Stolze: "Soft Navigation in electronic product catalogs," International Journal on Digital Libraries 3(1), pp. 60--66, 2000.Google ScholarCross Ref
- M. Torrens, R. Weigel and B. Faltings: "Java Constraint Library: bringing constraint technology on the Internet using the Java language," Workshop on Constraints and Agents (AAAI97), AAAI Technical Report WS-97-05, July 1997.Google Scholar
- M. Torrens, B. Faltings and P. Pu: "SmartClients: Constraint Satisfaction as a Paradigm for Scaleable Intelligent Information Systems," CONSTRAINTS 7, p. 49--69, 2002. Google ScholarDigital Library
- M. Torrens, P. Hertzog, L. Samson and B. Faltings: "reality: a Scalable Intelligent Travel Planer, Decision Support for the Business Traveler," 18th ACM Symposium on Applied Computing, Melbourne, 2003. Google ScholarDigital Library
Index Terms
- Designing example-critiquing interaction
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