How can I model traffic rules for autonomous vehicle interaction using multi-agent systems?
Mind the Gaps: Logical English, Prolog, and Multi-agent Systems for Autonomous Vehicles
February 14, 2025
https://arxiv.org/pdf/2502.09216This paper proposes a modular system for representing and reasoning with traffic laws for autonomous vehicles (AVs), focusing on UK Highway Code rules at junctions. The system aims to ensure AV behavior aligns with human driver expectations and legal requirements in mixed traffic environments. It uses Logical English (a controlled natural language) to encode rules, Prolog for internal representation and reasoning, and NetLogo for multi-agent simulation.
Key points for LLM-based multi-agent systems:
- Natural Language Interface: Logical English provides a human-readable way to encode rules, bridging the gap between legal text and executable code, suggesting potential applications for LLMs in rule representation and interpretation.
- Logic-Based Reasoning: The Prolog component allows for formal reasoning about traffic situations and violations, aligning with the symbolic reasoning capabilities of LLMs.
- Multi-Agent Simulation: NetLogo allows simulating interactions between human drivers and AVs, highlighting the potential of multi-agent environments as testing grounds for LLM-driven agents.
- Violation Detection and Legal Reasoning: The system includes monitors that detect potential violations and a validator that assesses whether these violations are punishable, demonstrating a framework for integrating legal reasoning into LLM-agent decision-making.
- Human-Agent Interaction: A key concern is designing AV behavior that is predictable and understandable by human drivers, suggesting a direction for research in LLM-based agent communication and explainability.