Publication
The Constitutional Filter
Simon Kohaut; Felix Divo; Benedict Flade; Devendra Singh Dhami; Julian Eggert; Kristian Kersting
In: Computing Research Repository eprint Journal (CoRR), Vol. abs/2412.18347, Pages 1-8, arXiv, 2024.
Abstract
Predicting agents impacted by legal policies, phys-
ical limitations, and operational preferences is inherently dif-
ficult. 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.