Multiattribute Bayesian optimization with interactive preference learning
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Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics, PMLR 108:44964507, 2020.
Abstract
We consider blackbox global optimization of timeconsumingtoevaluate functions on behalf of a decisionmaker (DM) whose preferences must be learned. Each feasible design is associated with a timeconsumingtoevaluate vector of attributes and each vector of attributes is assigned a utility by the DM’s utility function, which may be learned approximately using preferences expressed over pairs of attribute vectors. Past work has used a point estimate of this utility function as if it were errorfree within singleobjective optimization. However, utility estimation errors may yield a poor suggested design. Furthermore, this approach produces a single suggested ‘best’ design, whereas DMs often prefer to choose from a menu. We propose a novel multiattribute Bayesian optimization with preference learning approach. Our approach acknowledges the uncertainty in preference estimation and implicitly chooses designs to evaluate that are good not just for a single estimated utility function but a range of likely ones. The outcome of our approach is a menu of designs and evaluated attributes from which the DM makes a final selection. We demonstrate the value and flexibility of our approach in a variety of experiments.
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