Local Utility Elicitation in GAI Models

TL;DR

This paper introduces a practical method for eliciting parameters of GAI utility models using local queries, leveraging GAI structure to improve efficiency and extendability in preference elicitation.

Contribution

It proposes a novel local utility elicitation procedure for GAI models that reduces reliance on global queries and incorporates probabilistic uncertainty handling.

Findings

Effective local query-based elicitation demonstrated in experiments

Framework extends to probabilistic utility parameter uncertainty

Improves efficiency over traditional global elicitation methods

Abstract

Structured utility models are essential for the effective representation and elicitation of complex multiattribute utility functions. Generalized additive independence (GAI) models provide an attractive structural model of user preferences, offering a balanced tradeoff between simplicity and applicability. While representation and inference with such models is reasonably well understood, elicitation of the parameters of such models has been studied less from a practical perspective. We propose a procedure to elicit GAI model parameters using only "local" utility queries rather than "global" queries over full outcomes. Our local queries take full advantage of GAI structure and provide a sound framework for extending the elicitation procedure to settings where the uncertainty over utility parameters is represented probabilistically. We describe experiments using a myopic value-of-information approach to elicitation in a large GAI model.