Context-Triggered Contingency Games for Strategic Multi-Agent Interaction

2025-12-03

AI Summary

Addressing the challenge of reconciling long-term strategic objectives with short-term dynamic adaptation in autonomous multi-agent systems, this paper proposes a context-triggered emergency game framework. The framework adopts a two-layer architecture: an upper layer employs temporal logic to synthesize safety- and progress-guaranteeing strategy templates, ensuring formal correctness of high-level logical goals; a lower layer leverages factor graph modeling coupled with real-time model predictive control to enable low-latency, scalable dynamic game solving. Its key innovation lies in the principled integration of formal strategic planning with online emergency response, balancing theoretical rigor and engineering practicality. Evaluations in simulation and real-world experiments on autonomous driving and robot navigation demonstrate significant improvements in interaction reliability, response efficiency, and environmental adaptability.

Technology Category

• Multiagent Systems: Multiagent Planning
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Planning, Routing, and Scheduling: Temporal Planning

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

We address the challenge of reliable and efficient interaction in autonomous multi-agent systems, where agents must balance long-term strategic objectives with short-term dynamic adaptation. We propose context-triggered contingency games, a novel integration of strategic games derived from temporal logic specifications with dynamic contingency games solved in real time. Our two-layered architecture leverages strategy templates to guarantee satisfaction of high-level objectives, while a new factor-graph-based solver enables scalable, real-time model predictive control of dynamic interactions. The resulting framework ensures both safety and progress in uncertain, interactive environments. We validate our approach through simulations and hardware experiments in autonomous driving and robotic navigation, demonstrating efficient, reliable, and adaptive multi-agent interaction.

Problem

• Balancing long-term strategy with short-term adaptation in multi-agent systems
• Ensuring safety and progress in uncertain interactive environments
• Enabling scalable real-time control for autonomous driving and robotics

Innovation

• Two-layered architecture with strategy templates
• Factor-graph-based solver for real-time control
• Context-triggered contingency games integration