Predicting agents impacted by legal policies, physical limitations, and operational preferences is inherently difficult. In recent years, neuro-symbolic methods have emerged, integrating machine learning and symbolic reasoning models into end-to-end learnable systems. Hereby, a promising avenue for expressing high-level constraints over multi-modal input data in robotics has opened up. This work introduces an approach for Bayesian estimation of agents expected to comply with a human-interpretable neuro-symbolic model we call its Constitution. Hence, we present the Constitutional Filter (CoFi), leading to improved tracking of agents by leveraging expert knowledge, incorporating deep learning architectures, and accounting for environmental uncertainties. CoFi extends the general, recursive Bayesian estimation setting, ensuring compatibility with a vast landscape of established techniques such as Particle Filters. To underpin the advantages of CoFi, we evaluate its performance on real-world marine traffic data. Beyond improved performance, we show how CoFi can learn to trust and adapt to the level of compliance of an agent, recovering baseline performance even if the assumed Constitution clashes with reality.

翻译：预测受法律政策、物理限制及操作偏好影响的智能体行为具有本质困难。近年来，神经符号方法逐渐兴起，通过将机器学习与符号推理模型整合为端到端可学习系统，为在机器人学中表达多模态输入数据的高层约束开辟了前景广阔的途径。本文提出一种贝叶斯估计方法，用于预测预期遵守人类可解释神经符号模型（我们称其为"宪法"）的智能体。据此，我们提出宪法过滤器（CoFi），该方法通过利用专家知识、整合深度学习架构并考虑环境不确定性，实现了对智能体跟踪性能的提升。CoFi扩展了通用的递归贝叶斯估计框架，确保与粒子滤波器等大量现有技术保持兼容。为验证CoFi的优势，我们在真实世界海上交通数据上评估其性能。除性能提升外，我们还展示了CoFi如何学习信任并适应智能体的合规程度，即使在假设宪法与现实冲突时仍能恢复基线性能。