Explaining Strategic Decisions in Multi-Agent Reinforcement Learning for Aerial Combat Tactics
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908367300067328 |
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| author | Selmonaj, Ardian Antonucci, Alessandro Schneider, Adrian Rüegsegger, Michael Sommer, Matthias |
| author_facet | Selmonaj, Ardian Antonucci, Alessandro Schneider, Adrian Rüegsegger, Michael Sommer, Matthias |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_11311 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Explaining Strategic Decisions in Multi-Agent Reinforcement Learning for Aerial Combat Tactics Selmonaj, Ardian Antonucci, Alessandro Schneider, Adrian Rüegsegger, Michael Sommer, Matthias Multiagent Systems Artificial Intelligence Machine Learning 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. |
| title | Explaining Strategic Decisions in Multi-Agent Reinforcement Learning for Aerial Combat Tactics |
| topic | Multiagent Systems Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2505.11311 |