用贝叶斯方法少问几轮就找到决策者最满意解
Bayesian preference elicitation for decision support in multiobjective optimization
- 通过成对比较构建决策者偏好模型
- 仅需少量交互即可定位高价值解
- 适合需要快速决策的多目标优化场景
我们提出一种新方法，帮助决策者从多目标优化的帕累托集（Pareto set）中高效识别偏好解。该方法基于贝叶斯模型，利用成对比较估计决策者的效用函数，并通过平衡探索与利用的查询策略，逐步引导发现高价值解。方法灵活，既可交互式使用，也可在标准多目标优化完成后进行后验分析。最终生成一个高质量解的简化候选集，降低决策负担。在最多九个目标的测试问题上，本方法以极少查询次数即找到高价值解，表现优于现有方法。我们已开源实现，便于社区采用。
原文摘要 · Abstract (English)
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.
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