标题： 扩展结构因果模型以用于自主具身系统 显示英文标题

标题： Extending Structural Causal Models for Use in Autonomous Embodied Systems

摘要： 大量工作已经完成，以在多个领域开发因果推理技术，然而在自主系统中利用因果关系仍处于初级阶段。 自主系统通过使用结构因果模型（SCMs）等表示方式，将从因果关系的整合中获益良多。 该系统将获得更高层次的透明度，它将能够对结果进行事后解释，并有助于对外生变量的在线推断。 这些特性要么直接有利于自主系统，要么是在建立公众信任和制定法规过程中的重要步骤。 为此，我们提出一个案例研究，其中描述了一个由SCMs组成的模块化自动驾驶系统。 处理这项任务需要考虑在处理一个复杂且庞大的系统时面临的诸多挑战，该系统必须长时间独立运行。 在这里，我们描述这些挑战，并提出解决方案。 第一个是SCM上下文，其余的是三个新的变量类别——其中两个基于函数式编程中的单子（monads）。 最后，我们通过展示自动驾驶系统因果能力的一个示例应用来结束本文。 在这个示例中，我们的目标是在假设的道路碰撞事件中，确定车辆代理之间的责任归属。 显示英文摘要

摘要： Much work has been done to develop causal reasoning techniques across a number of domains, however the utilisation of causality within autonomous systems is still in its infancy. Autonomous systems would greatly benefit from the integration of causality through the use of representations such as structural causal models (SCMs). The system would be afforded a higher level of transparency, it would enable post-hoc explanations of outcomes, and assist in the online inference of exogenous variables. These qualities are either directly beneficial to the autonomous system or a valuable step in building public trust and informing regulation. To such an end we present a case study in which we describe a module-based autonomous driving system comprised of SCMs. Approaching this task requires considerations of a number of challenges when dealing with a system of great complexity and size, that must operate for extended periods of time by itself. Here we describe these challenges, and present solutions. The first of these is SCM contexts, with the remainder being three new variable categories -- two of which are based upon functional programming monads. Finally, we conclude by presenting an example application of the causal capabilities of the autonomous driving system. In this example, we aim to attribute culpability between vehicular agents in a hypothetical road collision incident.