Title: Learning What Matters Now: Dynamic Preference Inference under Contextual Shifts
Primary area: applications to robotics, autonomy, planning
Keywords: Dynamic Value Preference Inference, Cognitive Modeling, Adaptive Decision-Making

Abstract: Human decision-making is guided by dynamically shifting value priorities rather than fixed objective functions. Existing computational models often assume static optimization targets, overlooking phenomena such as preference drift, context-sensitive trade-offs, and goal re-evaluation. In this work, we introduce a cognitive-inspired framework that treats preferences as latent states inferred under uncertainty. The agent maintains a Bayesian belief over preferences, continuously refines this belief from past experience, and acts according to the most probable preference configuration. Crucially, inference and control are trained jointly, ensuring that preference adaptation remains aligned with long-term utility while grounded in interpretable value dimensions. This formulation enables agents to autonomously re-balance goals as circumstances change, without externally specified weight schedules. Experiments demonstrate superior adaptability to preference shifts and changing constraints, highlighting both the practical effectiveness and cognitive plausibility of our approach.