Toward experiential utility elicitation for interface customization

Bowen Hui, Craig Boutilier
Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence, PMLR R6:298-305, 2008.

Abstract

User preferences for automated assistance often vary widely, depending on the situation, and quality or presentation of help. Developing effective models to learn individual preferences online requires domain models that associate observations of user behavior with their utility functions, which in turn can be constructed using utility elicitation techniques. However, most elicitation methods ask for users’ predicted utilities based on hypothetical scenarios rather than more realistic experienced utilities. This is especially true in interface customization, where users are asked to assess novel interface designs. We propose experiential utility elicitation methods for customization and compare these to predictive methods. As experienced utilities have been argued to better reflect true preferences in behavioral decision making, the purpose here is to investigate accurate and efficient procedures that are suitable for software domains. Unlike conventional elicitation, our results indicate that an experiential approach helps people understand stochastic outcomes, as well as better appreciate the sequential utility of intelligent assistance.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR6-hui08a, title = {Toward experiential utility elicitation for interface customization}, author = {Hui, Bowen and Boutilier, Craig}, booktitle = {Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence}, pages = {298--305}, year = {2008}, editor = {McAllester, David A. and Myllymäki, Petri}, volume = {R6}, series = {Proceedings of Machine Learning Research}, month = {09--12 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r6/main/assets/hui08a/hui08a.pdf}, url = {https://proceedings.mlr.press/r6/hui08a.html}, abstract = {User preferences for automated assistance often vary widely, depending on the situation, and quality or presentation of help. Developing effective models to learn individual preferences online requires domain models that associate observations of user behavior with their utility functions, which in turn can be constructed using utility elicitation techniques. However, most elicitation methods ask for users’ predicted utilities based on hypothetical scenarios rather than more realistic experienced utilities. This is especially true in interface customization, where users are asked to assess novel interface designs. We propose experiential utility elicitation methods for customization and compare these to predictive methods. As experienced utilities have been argued to better reflect true preferences in behavioral decision making, the purpose here is to investigate accurate and efficient procedures that are suitable for software domains. Unlike conventional elicitation, our results indicate that an experiential approach helps people understand stochastic outcomes, as well as better appreciate the sequential utility of intelligent assistance.}, note = {Reissued by PMLR on 09 October 2024.} }
Endnote
%0 Conference Paper %T Toward experiential utility elicitation for interface customization %A Bowen Hui %A Craig Boutilier %B Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2008 %E David A. McAllester %E Petri Myllymäki %F pmlr-vR6-hui08a %I PMLR %P 298--305 %U https://proceedings.mlr.press/r6/hui08a.html %V R6 %X User preferences for automated assistance often vary widely, depending on the situation, and quality or presentation of help. Developing effective models to learn individual preferences online requires domain models that associate observations of user behavior with their utility functions, which in turn can be constructed using utility elicitation techniques. However, most elicitation methods ask for users’ predicted utilities based on hypothetical scenarios rather than more realistic experienced utilities. This is especially true in interface customization, where users are asked to assess novel interface designs. We propose experiential utility elicitation methods for customization and compare these to predictive methods. As experienced utilities have been argued to better reflect true preferences in behavioral decision making, the purpose here is to investigate accurate and efficient procedures that are suitable for software domains. Unlike conventional elicitation, our results indicate that an experiential approach helps people understand stochastic outcomes, as well as better appreciate the sequential utility of intelligent assistance. %Z Reissued by PMLR on 09 October 2024.
APA
Hui, B. & Boutilier, C.. (2008). Toward experiential utility elicitation for interface customization. Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R6:298-305 Available from https://proceedings.mlr.press/r6/hui08a.html. Reissued by PMLR on 09 October 2024.

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