Cooperative Resilience in Artificial Intelligence Multiagent Systems

TL;DR

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.

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.

Figures (7)

• Figure 1: Keyword map of resilience across diverse fields and contexts, addressing guiding questions.
• Figure 2: Diagram illustrating the proposed methodology for measuring cooperative resilience.
• Figure 3: Diagram summarizing the reasoning process flow within the LLM architecture, leading to the action-taking phase of each agent. The diagram is inspired by the architecture proposed in mosquera2024LLM.
• Figure 4: Performance and reference curves: The blue line represents the mean performance curve over five episodes, while the orange line indicates the mean reference curve. The shaded regions correspond to the standard deviation. The red dashed line marks the occurrence of the disruptive event. The top row (a, b, c, d) shows the results of agents trained with RL, while the bottom row (e, f, g, h) displays the results of LLM-based models. (a) and (e) depict the apples alive per capita in experiment E9. (b) and (f) show the trees alive per capita in experiment E2. (c) and (g) illustrate the Gini Equality Index in experiment E5. Finally, (d) and (h) present the Collective Hunger Level in experiment E7.
• Figure 5: Cooperative Resilience Map: This heatmap illustrates the impact of varying the number of disruptive events (1, 2, or 3) and the disturbance magnitude ($v_s$) on system resilience. The map uses darker colors to represent lower resilience values. Figure (a) shows results for the RL approach, while (b) displays results for the LLM.

Theorems & Definitions (1)

• Definition 1