Context-Triggered Contingency Games for Strategic Multi-Agent Interaction

TL;DR

This paper introduces context-triggered contingency games, a novel framework combining strategic and dynamic models to enable reliable, safe, and adaptive multi-agent interactions in real-time environments.

Contribution

It presents a two-layered architecture with strategy templates and a factor-graph-based solver for scalable, real-time control in multi-agent systems.

Findings

Validated through simulations and hardware experiments

Demonstrated efficiency and reliability in autonomous driving and robotic navigation

Ensured safety and progress in uncertain environments

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.