Cooperative Resilience in Artificial Intelligence Multiagent Systems

TL;DR

This paper defines and measures 'cooperative resilience' in multi-agent AI systems, proposing a methodology validated through experiments with reinforcement learning and language model agents facing environmental disruptions.

Contribution

It introduces a clear definition and a quantitative measurement methodology for cooperative resilience in AI multi-agent systems, filling a key research gap.

Findings

Resilience metrics effectively analyze system responses to disruptions.

The methodology applies to RL-based and LLM-augmented agents.

Results highlight the importance of resilience in AI system robustness.

Abstract

Resilience refers to the ability of systems to withstand, adapt to, and recover from disruptive events. While studies on resilience have attracted significant attention across various research domains, the precise definition of this concept within the field of cooperative artificial intelligence remains unclear. This paper addresses this gap by proposing a clear definition of `cooperative resilience' and outlining a methodology for its quantitative measurement. The methodology is validated in an environment with RL-based and LLM-augmented autonomous agents, subjected to environmental changes and the introduction of agents with unsustainable behaviors. These events are parameterized to create various scenarios for measuring cooperative resilience. The results highlight the crucial role of resilience metrics in analyzing how the collective system prepares for, resists, recovers from, sustains well-being, and transforms in the face of disruptions. These findings provide foundational insights into the definition, measurement, and preliminary analysis of cooperative resilience, offering significant implications for the broader field of AI. Moreover, the methodology and metrics developed here can be adapted to a wide range of AI applications, enhancing the reliability and effectiveness of AI in dynamic and unpredictable environments.