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

2025-05-16

AI Summary

To address the lack of interpretability in multi-agent reinforcement learning (MARL) decision-making within air combat simulation—leading to diminished human trust, safety risks, and impaired human-AI collaboration—this paper pioneers the systematic adaptation of eXplainable AI (XAI) techniques to tactical-level aerial engagement scenarios. We propose a three-tiered explainability framework integrating post-hoc explanation methods (SHAP and attention visualization), behavioral cloning-based policy modeling, and human-in-the-loop validation. This framework establishes a semantic mapping bridge between AI-generated tactical decisions and human strategic reasoning. Experimental evaluation demonstrates that our approach significantly enhances decision transparency, achieving over 92% alignment with expert human pilots’ tactical judgments in simulated engagements. The framework provides a reproducible, empirically verifiable pathway for deploying trustworthy MARL systems in high-stakes, safety-critical military applications.

Technology Category

• Humans and AI: Explainable AI (XAI) for Human Understanding
• Multiagent Systems: Adversarial Agents
• Philosophy and Ethics of AI: Accountability, Interpretability & Explainability

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Agentic search
• Responsible Web: Machine-in-the-loop, human agency and autonomy

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.

Problem

• Lack of explainability in MARL for military aerial combat
• Need transparency to align AI tactics with human strategies
• Enhancing trust and safety in MARL-based defense operations

Innovation

• Adapting explainability techniques to aerial combat
• Linking AI tactics with human-understandable reasoning
• Enhancing MARL transparency for military deployment