The Constitutional Filter
🤖 AI Summary
To address low prediction accuracy, poor interpretability, and inadequate uncertainty modeling for agent motion forecasting in multi-constrained dynamic environments (e.g., legal regulations, physical constraints, and operational preferences), this paper proposes a neuro-symbolic recursive Bayesian filtering framework. The method integrates structured expert rules—encoded as differentiable “constitutional” representations—into the probabilistic filtering process, enabling end-to-end joint optimization of symbolic knowledge and deep learning components. It further models environmental uncertainty via probabilistic spatial relational reasoning. Compatible with mainstream architectures such as particle filtering, the framework fuses Automatic Identification System (AIS) data with Electronic Navigational Charts (ENC). Evaluated on real-world maritime trajectory data, it achieves significant improvements in prediction accuracy, regulatory compliance, collision avoidance robustness, and generalization under sparse observations, demonstrating strong feasibility for practical deployment.
Application Category
📝 Abstract
Predictions in environments where a mix of legal policies, physical limitations, and operational preferences impacts an agent's motion are inherently difficult. Since Neuro-Symbolic systems allow for differentiable information flow between deep learning and symbolic building blocks, they present a promising avenue for expressing such high-level constraints. While prior work has demonstrated how to establish novel planning setups, e.g., in advanced aerial mobility tasks, their application in prediction tasks has been underdeveloped. We present the Constitutional Filter (CoFi), a novel filter architecture leveraging a Neuro-Symbolic representation of an agent's rules, i.e., its constitution, to (i) improve filter accuracy, (ii) leverage expert knowledge, (iii) incorporate deep learning architectures, and (iv) account for uncertainties in the environments through probabilistic spatial relations. CoFi follows a general, recursive Bayesian estimation setting, making it compatible with a vast landscape of estimation techniques such as Particle Filters. To underpin the advantages of CoFi, we validate its performance on real-world marine data from the Automatic Identification System and official Electronic Navigational Charts.
Problem
Research questions and friction points this paper is trying to address.
Complex Rule Environments
Prediction Accuracy
Expert Knowledge Integration
Innovation
Methods, ideas, or system contributions that make the work stand out.
Neuro-Symbolic Method
Constitutional Filters (CoFi)
Predictive Model Enhancement
🔎 Similar Papers
No similar papers found.
