在不确定性下评估轨迹时，考虑系统与环境的相互作用，提升决策可解释性。
Risk-Aware Rulebooks for Multi-Objective Trajectory Evaluation under Uncertainty
- 构建风险感知的形式化框架，显式建模系统轨迹对环境的影响
- 证明轨迹集上存在全序关系，避免循环偏好，确保决策一致性
- 适用于自动驾驶等多目标复杂场景，增强决策过程的可解释性
我们提出一种风险感知的形式化框架，用于在系统与环境存在不确定交互的情况下评估系统轨迹。该框架支持不确定性下的推理，并系统处理需求与目标间的复杂关系，包括层级优先级和不可比较性。不同于将环境视为外生噪声，我们显式建模每个系统轨迹对环境的影响，并在由此产生的环境响应分布下评估轨迹。我们证明该形式化在系统轨迹集合上诱导出一个全序关系，确保一致性并防止循环偏好。最后，通过自动驾驶示例说明该方法如何通过阐明轨迹选择背后的逻辑来增强可解释性。
原文摘要 · Abstract (English)
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.
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