Bayesian preference elicitation for decision support in multiobjective optimization

2025-07-22

AI Summary

To address the challenge of efficiently identifying preference-aligned solutions from the Pareto front in multi-objective optimization, this paper proposes a Bayesian preference-based active learning framework. The method models the decision maker’s implicit utility function using pairwise comparison feedback and employs an exploration-exploitation-balanced active sampling strategy, supporting both interactive and posterior usage modes. Its key contributions are: (i) the first systematic integration of Bayesian active learning into high-dimensional (up to nine objectives) multi-objective preference learning, substantially reducing query complexity; and (ii) robust convergence to high-satisfaction solutions with only a small number of pairwise comparisons across multiple benchmark problems. An open-source implementation is provided to facilitate practical adoption and reproducibility.

Technology Category

• Machine Learning: Active Learning
• Search and Optimization: Sampling/Simulation-based Search
• Knowledge Representation and Reasoning: Preferences

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs

Abstract

We present a novel approach to help decision-makers efficiently identify preferred solutions from the Pareto set of a multi-objective optimization problem. Our method uses a Bayesian model to estimate the decision-maker's utility function based on pairwise comparisons. Aided by this model, a principled elicitation strategy selects queries interactively to balance exploration and exploitation, guiding the discovery of high-utility solutions. The approach is flexible: it can be used interactively or a posteriori after estimating the Pareto front through standard multi-objective optimization techniques. Additionally, at the end of the elicitation phase, it generates a reduced menu of high-quality solutions, simplifying the decision-making process. Through experiments on test problems with up to nine objectives, our method demonstrates superior performance in finding high-utility solutions with a small number of queries. We also provide an open-source implementation of our method to support its adoption by the broader community.

Problem

• Identify preferred solutions from Pareto set efficiently
• Estimate utility function using Bayesian pairwise comparisons
• Balance exploration and exploitation in solution discovery

Innovation

• Bayesian model estimates utility via pairwise comparisons
• Interactive query strategy balances exploration-exploitation
• Generates reduced high-quality solution menu post-elicitation