Minimax regret based elicitation of generalized additive utilities

Darius Braziunas
Department of Computer Science
University of Toronto
Toronto, ON M5S 3H5
email: darius@cs.toronto.edu

Craig Boutilier
Department of Computer Science
University of Toronto
Toronto, ON M5S 3H5
email: cebly@cs.toronto.edu

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.

To appear, UAI-07

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