提出连续空间交互中的因果责任度量方法，助力智能体安全决策
Feasible Action Space Reduction for Quantifying Causal Responsibility in Continuous Spatial Interactions
- 将离散动作空间的因果责任度量拓展到连续空间交互
- 在典型空间冲突场景中验证了该方法的有效性
- 适用于回溯责任分析与前向决策引导，适合自动驾驶研究
理解一个智能体对另一个智能体的因果影响，对于将自动驾驶车辆和移动机器人等AI系统安全部署于人类环境至关重要。现有因果责任模型多基于离散动作的简化场景，难以应用于真实空间交互。本文基于空间交互智能体嵌入场景且每时刻需执行动作的假设，将先前在网格世界中提出的可行动作空间缩减（Feasible Action-Space Reduction, FeAR）度量扩展至连续动作空间，用于量化连续空间交互中的因果责任。通过典型空间共享冲突场景的演示，展示了该度量在回溯责任分析及前向责任估计中的应用价值，后者可用于指导智能体决策。结果表明，该度量在设计和工程化人工智能体、评估人机交互中的责任方面具有潜力。
原文摘要 · Abstract (English)
Understanding the causal influence of one agent on another agent is crucial for safely deploying artificially intelligent systems such as automated vehicles and mobile robots into human-inhabited environments. Existing models of causal responsibility deal with simplified abstractions of scenarios with discrete actions, thus, limiting real-world use when understanding responsibility in spatial interactions. Based on the assumption that spatially interacting agents are embedded in a scene and must follow an action at each instant, Feasible Action-Space Reduction (FeAR) was proposed as a metric for causal responsibility in a grid-world setting with discrete actions.Since real-world interactions involve continuous action spaces, this paper proposes a formulation of the FeAR metric for measuring causal responsibility in space-continuous interactions. We illustrate the utility of the metric in prototypical space-sharing conflicts, and showcase its applications for analysing backward-looking responsibility and in estimating forward-looking responsibility to guide agent decision making. Our results highlight the potential of the FeAR metric for designing and engineering artificial agents, as well as for assessing the responsibility of agents around humans.
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