We present a novel framework to generate causal explanations for the decisions of agents in stochastic sequential multi-agent environments. Explanations are given via natural language conversations answering a wide range of user queries and requiring associative, interventionist, or counterfactual causal reasoning. Instead of assuming any specific causal graph, our method relies on a generative model of interactions to simulate counterfactual worlds which are used to identify the salient causes behind decisions. We implement our method for motion planning for autonomous driving and test it in simulated scenarios with coupled interactions. Our method correctly identifies and ranks the relevant causes and delivers concise explanations to the users' queries.

翻译：我们提出了一种新颖的框架，用于在随机序贯多智能体环境中生成智能体决策的因果解释。解释通过自然语言对话呈现，能够回答用户各种类型的查询，并需要关联性、干预性或反事实因果推理。我们的方法不依赖于任何特定的因果图，而是利用交互生成模型来模拟反事实世界，从而识别决策背后的关键原因。我们在自动驾驶的运动规划中实现了该方法，并在具有耦合交互的模拟场景中进行了测试。我们的方法能够正确识别并排序相关原因，针对用户的查询提供简洁的解释。