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 for causal responsibility among agents. Specifically, we look at ex-post causal responsibility for simultaneous actions. We propose the use of Moves de Rigueur - 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学与哲学层面的责任概念依据，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（例如交通场景中的交互）的因果责任度量指标。在此类交互中，一个智能体可通过缩减另一智能体的可行行动空间来影响后者。因此，我们提出将可行行动空间缩减（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溯因因果责任，并引入"常规行动集"（Moves de Rigueur）——一套智能体应遵循的规范性行动集合——来建模规范对责任分配的影响。我们在网格世界仿真中应用该度量指标开展空间交互实验，展示行动、情境与规范如何影响智能体被赋予的因果责任。最后，我们演示该度量在复杂多智能体交互中的应用。我们认为，FeAR度量指标是迈向跨学科责任量化框架的重要一步，而该框架正是确保人机系统中安全性与有意义的人类控制所必需的。