Modelling causal responsibility in multi-agent spatial interactions is crucial for safety and efficiency of interactions of humans with autonomous agents. However, current formal metrics and models of responsibility either lack grounding in ethical and philosophical concepts of responsibility, or cannot be applied to spatial interactions. In this work we propose a metric of causal responsibility which is tailored to multi-agent spatial interactions, for instance interactions in traffic. In such interactions, a given agent can, by reducing another agent's feasible action space, influence the latter. Therefore, we propose feasible action space reduction (FeAR) as a metric of causal responsibility among agents. Specifically, we look at ex-post causal responsibility for simultaneous actions. We propose the use of Moves de Rigueur (MdR) - a consistent set of prescribed actions for agents - to model the effect of norms on responsibility allocation. We apply the metric in a grid world simulation for spatial interactions and show how the actions, contexts, and norms affect the causal responsibility ascribed to agents. Finally, we demonstrate the application of this metric in complex multi-agent interactions. We argue that the FeAR metric is a step towards an interdisciplinary framework for quantifying responsibility that is needed to ensure safety and meaningful human control in human-AI systems.

翻译：在多智能体空间交互中建模因果责任对于人类与自主智能体交互的安全性和效率至关重要。然而，当前的因果责任形式化度量与模型要么缺乏伦理和哲学概念层面的责任基础，要么无法应用于空间交互场景。本文提出一种专门针对多智能体空间交互（如交通交互场景）的因果责任度量方法。在此类交互中，特定智能体可通过缩减其他智能体的可行动作空间来影响后者。因此，我们提出将可行动作空间缩减（FeAR）作为智能体间因果责任的度量指标。具体而言，我们关注同步行动的事后因果责任，提出采用规范动作集（MdR）——即智能体的一组一致性规定动作——来建模规范对责任分配的影响。通过网格世界空间交互仿真实验，我们展示了动作、情境和规范如何影响归因于智能体的因果责任。最后，我们论证了该度量在复杂多智能体交互中的应用。我们认为FeAR度量是向建立量化责任的跨学科框架迈出的重要一步，这一框架对于确保人机系统中安全性与有意义的人类控制至关重要。