Risk-Aware Rulebooks for Multi-Objective Trajectory Evaluation under Uncertainty

2026-03-04

AI Summary

This work addresses the challenge of trajectory evaluation in systems interacting uncertainly with their environment, where existing approaches struggle to simultaneously handle multiple objectives, prioritize competing criteria, manage incommensurable goals, and account for the system’s feedback on the environment. To overcome these limitations, the paper proposes a risk-aware formal framework that models the environment as endogenously responsive rather than as exogenous noise, explicitly capturing how candidate trajectories influence environmental dynamics. Evaluation is performed based on the induced distribution of environmental responses. The framework systematically accommodates hierarchical priorities and incommensurable objectives, and the resulting preference relation is rigorously shown to constitute a preorder, thereby precluding cyclic preferences. Validation in an autonomous driving scenario demonstrates that the approach enhances the interpretability, consistency, and reasonableness of trajectory selection.

Technology Category

• Reasoning under Uncertainty: Sequential Decision Making
• Knowledge Representation and Reasoning: Preferences
• Planning, Routing, and Scheduling: Model-Based Reasoning

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

We present a risk-aware formalism for evaluating system trajectories in the presence of uncertain interactions between the system and its environment. The proposed formalism supports reasoning under uncertainty and systematically handles complex relationships among requirements and objectives, including hierarchical priorities and non-comparability. Rather than treating the environment as exogenous noise, we explicitly model how each system trajectory influences the environment and evaluate trajectories under the resulting distribution of environment responses. We prove that the formalism induces a preorder on the set of system trajectories, ensuring consistency and preventing cyclic preferences. Finally, we illustrate the approach with an autonomous driving example that demonstrates how the formalism enhances explainability by clarifying the rationale behind trajectory selection.

Problem

• risk-aware
• trajectory evaluation
• uncertainty
• multi-objective
• formalism

Innovation

• risk-aware
• trajectory evaluation
• uncertainty
• formalism
• multi-objective