Interactive Multi-Objective Probabilistic Preference Learning with Soft and Hard Bounds

2025-06-26

AI Summary

In high-risk multi-objective decision-making (e.g., radiotherapy), challenges include coupled soft/hard constraints, expensive evaluations, implicit user preferences, and the need for trustworthy solutions. To address these, we propose Active-MoSH—a two-tiered framework: a local module jointly models probabilistic preferences and soft/hard constraint boundaries; a global module, T-MoSH, actively explores high-quality yet overlooked Pareto-optimal solutions via multi-objective sensitivity analysis. This local-global synergy enables the first constraint-aware active preference optimization—ensuring strict feasibility while improving convergence speed and decision-maker trust. Evaluated on synthetic benchmarks and a real-world AI image selection user study, Active-MoSH significantly outperforms state-of-the-art interactive multi-objective methods, achieving faster convergence, higher preference modeling accuracy, and greater user confidence in recommended solutions.

Technology Category

• Constraint Satisfaction and Optimization: Constraint Optimization
• Machine Learning: Active Learning
• Search and Optimization: Sampling/Simulation-based Search

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
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

High-stakes decision-making involves navigating multiple competing objectives with expensive evaluations. For instance, in brachytherapy, clinicians must balance maximizing tumor coverage (e.g., an aspirational target or soft bound of >95% coverage) against strict organ dose limits (e.g., a non-negotiable hard bound of <601 cGy to the bladder), with each plan evaluation being resource-intensive. Selecting Pareto-optimal solutions that match implicit preferences is challenging, as exhaustive Pareto frontier exploration is computationally and cognitively prohibitive, necessitating interactive frameworks to guide users. While decision-makers (DMs) often possess domain knowledge to narrow the search via such soft-hard bounds, current methods often lack systematic approaches to iteratively refine these multi-faceted preference structures. Critically, DMs must trust their final decision, confident they haven't missed superior alternatives; this trust is paramount in high-consequence scenarios. We present Active-MoSH, an interactive local-global framework designed for this process. Its local component integrates soft-hard bounds with probabilistic preference learning, maintaining distributions over DM preferences and bounds for adaptive Pareto subset refinement. This is guided by an active sampling strategy optimizing exploration-exploitation while minimizing cognitive burden. To build DM trust, Active-MoSH's global component, T-MoSH, leverages multi-objective sensitivity analysis to identify potentially overlooked, high-value points beyond immediate feedback. We demonstrate Active-MoSH's performance benefits through diverse synthetic and real-world applications. A user study on AI-generated image selection further validates our hypotheses regarding the framework's ability to improve convergence, enhance DM trust, and provide expressive preference articulation, enabling more effective DMs.

Problem

• Balancing multiple objectives with soft-hard bounds in high-stakes decisions
• Refining multi-faceted preferences interactively under resource constraints
• Ensuring decision-maker trust by identifying potentially superior alternatives

Innovation

• Interactive local-global framework for multi-objective optimization
• Probabilistic preference learning with soft-hard bounds
• Active sampling strategy balancing exploration-exploitation