学习多智能体交互中的责任分配概率模型，揭示人类协作行为规律。
Learning Probabilistic Responsibility Allocations for Multi-Agent Interactions
- 基于条件变分自编码器与可微优化层，建模责任分配的多模态不确定性
- 在INTERACTION数据集上实现高精度轨迹预测，责任分配分布符合真实交互模式
- 为自动驾驶等系统提供可解释的责任推理机制，适合人机协同研究者
人类在互动场景中的行为不仅受个体目标影响，还受到与他人共享的约束（如安全）制约。理解人们如何分配责任——即为配合他人而偏离自身最优策略的程度——有助于设计更符合社会规范、值得信赖的自主系统。本文提出一种学习概率责任分配模型的方法，捕捉多智能体交互中固有的多模态不确定性。具体而言，该方法利用条件变分自编码器的隐空间，结合多智能体轨迹预测技术，学习在场景和智能体上下文条件下责任分配的概率分布。尽管缺乏真实的责任标签，模型仍可通过可微优化层将责任分配映射为可获得的控制动作，保持可训练性。我们在INTERACTION驾驶数据集上评估了该方法，结果表明其不仅具备强预测性能，还能通过责任视角提供对多智能体交互模式的可解释洞察。
原文摘要 · Abstract (English)
Human behavior in interactive settings is shaped not only by individual objectives but also by shared constraints with others, such as safety. Understanding how people allocate responsibility, i.e., how much one deviates from their desired policy to accommodate others, can inform the design of socially compliant and trustworthy autonomous systems. In this work, we introduce a method for learning a probabilistic responsibility allocation model that captures the multimodal uncertainty inherent in multi-agent interactions. Specifically, our approach leverages the latent space of a conditional variational autoencoder, combined with techniques from multi-agent trajectory forecasting, to learn a distribution over responsibility allocations conditioned on scene and agent context. Although ground-truth responsibility labels are unavailable, the model remains tractable by incorporating a differentiable optimization layer that maps responsibility allocations to induced controls, which are available. We evaluate our method on the INTERACTION driving dataset and demonstrate that it not only achieves strong predictive performance but also provides interpretable insights, through the lens of responsibility, into patterns of multi-agent interaction.
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