让无人机空战策略可解释，提升人机信任与训练效果
Explaining Strategic Decisions in Multi-Agent Reinforcement Learning for Aerial Combat Tactics
- 将可解释性技术适配到模拟空战场景，分析AI决策逻辑
- 揭示AI战术与人类思维的对应关系，增强行为透明度
- 适合军事训练与人机协同系统研发人员参考
人工智能正重塑战略规划，多智能体强化学习（MARL）使自主代理在复杂场景中实现协调。然而，在敏感军事应用中，其实际部署受限于缺乏可解释性——这是建立信任、保障安全并符合人类策略的关键因素。本文综述并评估了当前MARL可解释性方法在模拟空战场景中的进展。通过将多种可解释性技术应用于不同空战情境，我们获得对模型行为的解释性洞察。通过将AI生成的战术与人类可理解的推理关联，强调透明性对可靠部署和有意义人机交互的重要性。本研究不仅推动MARL在国防作战中的应用，也为军事人员培训提供可理解的分析支持。
原文摘要 · Abstract (English)
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.
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