2008-07-09-hui08a.md

title	abstract	layout	series	publisher	issn	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_editor	editor	bibtex_author	author	date	note	address	container-title	volume	genre	issued	pdf	extras
Toward experiential utility elicitation for interface customization	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.	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	hui08a	0	Toward experiential utility elicitation for interface customization	298	305	298-305	298	false	McAllester, David A. and Myllym{"a}ki, Petri	given family David A. McAllester given family Petri Myllymäki	Hui, Bowen and Boutilier, Craig	given family Bowen Hui given family Craig Boutilier	2008-07-09	Reissued by PMLR on 30 October 2024.		Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence	R6	inproceedings	date-parts 2008 7 9	http://proceedings.mlr.press/r6/hui08a/hui08a.pdf