Minimax regret based elicitation of generalized additive utilities

Abstract

We describe the semantic foundations for elicitation of generalized additively independent (GAI) utilities using the minimax regret criterion, and propose several new query types and strategies for this purpose. Computational feasibility is obtained by exploiting the local GAI structure in the model. Our results provide a practical approach for implementing preference-based constrained configuration optimization as well as effective search in multiattribute product databases.

In proceedings of the 23rd Conference on Uncertainty in Artificial Intelligence (UAI-07), Vancouver, 2007.

@inproceedings{BraziunasBoutilier-UAI07,
author = "Darius Braziunas and Craig Boutilier",
title = "Minimax regret based elicitation of generalized additive utilities",
booktitle = "Proceedings of the Twenty-third Conference on Uncertainty in Artificial
Intelligence (UAI-07)",
address = "Vancouver",
pages = "25--32",
year = "2007"}