We present CEMA: Causal Explanations in Multi-Agent systems; a general framework to create causal explanations for an agent's decisions in sequential multi-agent systems. The core of CEMA is a novel causal selection method inspired by how humans select causes for explanations. Unlike prior work that assumes a specific causal structure, CEMA is applicable whenever a probabilistic model for predicting future states of the environment is available. Given such a model, CEMA samples counterfactual worlds that inform us about the salient causes behind the agent's decisions. We evaluate CEMA on the task of motion planning for autonomous driving and test it in diverse simulated scenarios. We show that CEMA correctly and robustly identifies the causes behind decisions, even when a large number of agents is present, and show via a user study that CEMA's explanations have a positive effect on participant's trust in AVs and are rated at least as good as high-quality human explanations elicited from other participants.

翻译：我们提出CEMA（多智能体系统中的因果解释）框架，该通用框架可为序贯多智能体系统中智能体的决策生成因果解释。CEMA的核心是一种受人类选择因果解释方式启发的新型因果选择方法。与假定特定因果结构的先前工作不同，只要存在用于预测环境未来状态的概率模型，CEMA即可适用。基于该模型，CEMA通过采样反事实世界，揭示智能体决策背后的显著原因。我们在自动驾驶运动规划任务上评估CEMA，并在不同模拟场景中进行测试。实验表明，即使存在大量智能体，CEMA也能准确且稳健地识别决策背后的原因；用户研究进一步证明，CEMA生成的解释对参与者对自动驾驶车辆的信任度产生积极影响，其解释质量至少不逊于其他参与者提供的高质量人类解释。