# Right in Time: Reactive Reasoning in Regulated Traffic Spaces

arXiv: https://arxiv.org/abs/2603.03977 · HTML: https://mlsanity.com/paper/2603.03977/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 40 references that are in the map.
- First posted on arXiv 4 Mar 2026.

## Builds on (most cited first, 17 of 17)

- [Neural Probabilistic Logic Programming in DeepProbLog](https://mlsanity.com/paper/1907.08194/) (2019)
- [Robot Operating System 2: Design, Architecture, and Uses In The Wild](https://mlsanity.com/paper/2211.07752/) (2022)
- [NeurASP: Embracing Neural Networks into Answer Set Programming](https://mlsanity.com/paper/2307.07700/) (2023)
- [Scalable Neural-Probabilistic Answer Set Programming](https://mlsanity.com/paper/2306.08397/) (2023)
- [Answer Set Networks: Casting Answer Set Programming into Deep Learning](https://mlsanity.com/paper/2412.14814/) (2024)
- [Mission Design for Unmanned Aerial Vehicles using Hybrid Probabilistic Logic Programs](https://mlsanity.com/paper/2406.03454/) (2024)
- [Probabilistic Mission Design for Neuro-Symbolic Unmanned Aircraft Systems](https://mlsanity.com/paper/2501.01439/) (2024)
- [Towards Probabilistic Clearance, Explanation and Optimization](https://mlsanity.com/paper/2406.15088/) (2024)
- [Error Decomposition for Hybrid Localization Systems](https://mlsanity.com/paper/2410.14264/) (2024)
- [The Constitutional Filter: Bayesian Estimation of Compliant Agents](https://mlsanity.com/paper/2412.18347/) (2024)
- [StaR Maps: Unveiling Uncertainty in Geospatial Relations](https://mlsanity.com/paper/2412.18356/) (2024)
- [Scalar Field Mapping with Adaptive High-Intensity Region Avoidance](https://mlsanity.com/paper/2407.13543/) (2024)
- [Graph Neural Networks Need Cluster-Normalize-Activate Modules](https://mlsanity.com/paper/2412.04064/) (2024)
- [Reactive Knowledge Representation and Asynchronous Reasoning](https://mlsanity.com/paper/2602.05625/) (2026)
- [Hybrid Many-Objective Optimization in Probabilistic Mission Design for Compliant and Effective UAV Routing](https://mlsanity.com/paper/2412.18514/) (2024)
- [Exploring Neural Granger Causality with xLSTMs: Unveiling Temporal Dependencies in Complex Data](https://mlsanity.com/paper/2502.09981/) (2025)
- [The Constitutional Controller: Doubt-Calibrated Steering of Compliant Agents](https://mlsanity.com/paper/2507.15478/) (2025)

## Abstract (arXiv)

Exact inference in probabilistic First-Order Logic offers a promising yet computationally costly approach for regulating the behavior of autonomous agents in shared traffic spaces. While prior methods have combined logical and probabilistic data into decision-making frameworks, their application is often limited to pre-flight checks due to the complexity of reasoning across vast numbers of possible universes. In this work, we propose a reactive mission design framework that jointly considers uncertain environmental data and declarative, logical traffic regulations. By synthesizing Probabilistic Mission Design (ProMis) with reactive reasoning facilitated by Reactive Circuits (RC), we enable online, exact probabilistic inference over hybrid domains. Our approach leverages the Frequency of Change inherent in heterogeneous data streams to subdivide inference formulas into memoized, isolated tasks, ensuring that only the specific components affected by new sensor data are re-evaluated. In experiments involving both real-world vessel data and simulated drone traffic in dense urban scenarios, we demonstrate that our approach provides orders of magnitude in speedup over ProMis without reactive paradigms. This allows intelligent transportation systems, such as Unmanned Aircraft Systems (UAS), to actively assert safety and legal compliance during operations rather than relying solely on preparation procedures.