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Minimax regret based elicitation of generalized additive utilities
Published 20 Jun 2012 in cs.AI | (1206.5255v1)
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.
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