Right in Time: Reactive Reasoning in Regulated Traffic Spaces

2026-03-04

AI Summary

This work addresses the challenge of enabling online logical compliance reasoning for autonomous agents in shared transportation spaces, where existing approaches suffer from prohibitive computational complexity. The authors propose a reactive task design framework that, for the first time, integrates probabilistic first-order logic with reactive circuits. By monitoring the frequency of changes in perceptual data streams, the framework dynamically decomposes and memoizes reasoning tasks, selectively re-evaluating only those local logical formulas affected by new observations. This enables efficient incremental probabilistic inference in hybrid domains. Evaluated in realistic simulations involving maritime vessels and dense urban drone traffic, the method achieves speedups of several orders of magnitude over non-reactive ProMis, demonstrating, for the first time, real-time runtime guarantees for safety and regulatory compliance.

Technology Category

• Planning, Routing, and Scheduling: Model-Based Reasoning
• Knowledge Representation and Reasoning: Computational Complexity of Reasoning
• Reasoning under Uncertainty: Probabilistic Inference

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

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.

Problem

• reactive reasoning
• probabilistic inference
• traffic regulations
• autonomous agents
• online reasoning

Innovation

• Reactive Reasoning
• Probabilistic First-Order Logic
• Memoized Inference
• Hybrid Logical-Probabilistic Systems
• Online Exact Inference