Explaining Strategic Decisions in Multi-Agent Reinforcement Learning for Aerial Combat Tactics

TL;DR

This paper reviews and assesses explainability methods for Multi-Agent Reinforcement Learning in aerial combat, emphasizing their importance for trust, safety, and human-AI collaboration in military applications.

Contribution

It adapts and evaluates explainability techniques for MARL in simulated air combat, linking AI tactics with human-understandable reasoning to enhance transparency.

Findings

Explainability methods improve understanding of AI tactics.

Transparency is crucial for trust and deployment in military contexts.

Insights support training and strategic planning in defense.

Abstract

Artificial intelligence (AI) is reshaping strategic planning, with Multi-Agent Reinforcement Learning (MARL) enabling coordination among autonomous agents in complex scenarios. However, its practical deployment in sensitive military contexts is constrained by the lack of explainability, which is an essential factor for trust, safety, and alignment with human strategies. This work reviews and assesses current advances in explainability methods for MARL with a focus on simulated air combat scenarios. We proceed by adapting various explainability techniques to different aerial combat scenarios to gain explanatory insights about the model behavior. By linking AI-generated tactics with human-understandable reasoning, we emphasize the need for transparency to ensure reliable deployment and meaningful human-machine interaction. By illuminating the crucial importance of explainability in advancing MARL for operational defense, our work supports not only strategic planning but also the training of military personnel with insightful and comprehensible analyses.