用于空战战术的多智能体强化学习中战略决策的解释性研究
Explaining Strategic Decisions in Multi-Agent Reinforcement Learning for Aerial Combat Tactics
AI总结：
针对多智能体强化学习在军事场景中因可解释性不足难以部署的问题，研究适配多种可解释技术分析空战场景下的模型行为，强调透明度对可靠部署与人机交互的重要性，支撑战略规划与军事训练。
摘要：
人工智能（AI）正在重塑战略规划，多智能体强化学习（Multi-Agent Reinforcement Learning, MARL）可实现自主智能体在复杂场景下的协同。然而，其在敏感军事场景中的实际部署受限于可解释性的缺失，而可解释性是建立信任、保障安全以及与人类战略对齐的核心要素。本研究针对MARL可解释性方法的现有进展展开综述与评估，重点聚焦模拟空战场景。我们将多种可解释性技术适配到不同空战场景中，以获取关于模型行为的解释性洞见。通过将AI生成的战术与人类可理解的推理相关联，我们强调了透明度对于保障可靠部署及实现有意义的人机交互的必要性。本研究阐明了可解释性在推动MARL用于国防作战中的关键作用，不仅可为战略规划提供支撑，还能通过有深度、易理解的分析辅助军事人员训练。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
