让航空管制的AI决策可解释，提升人机协作信任度。
Explainable Reinforcement Learning for assisting Air Traffic Controllers
- 用显著性图分析RL智能体决策时依赖的关键输入特征。
- 在简化空管环境中训练智能体，实现避让禁飞区的路线规划。
- 为高风险场景下的AI可信应用提供可解释性思路，适合空管与安全领域研究者。
将AI融入医疗、自动驾驶和航空等高风险关键领域，并迈向更高程度自动化与无缝人机协作，建立对AI解决方案的信任至关重要。而信任与AI系统的可解释性密切相关。随着AI在各领域的快速进步，建立信任的挑战日益突出，促使人们对深度学习中的AI可解释性产生更多关注。本文旨在探索可解释性技术在强化学习（RL）算法中的应用，聚焦于安全关键的空中交通管制（ATC）领域。以一个简化的ATC环境作为初始测试平台，训练一个智能体使用强化学习算法，自主决策绕开禁飞区的替代飞行路径。作为初步的可解释性方法，采用显著性图（saliency map）揭示影响智能体决策的关键输入特征，从而增强对决策过程的理解。
原文摘要 · Abstract (English)
To effectively integrate AI into high-stakes, critical environments such as healthcare, autonomous driving, and aviation--and to advance toward higher levels of automation and seamless human-AI collaboration--building trust in AI-driven solutions is essential. Trust, in turn, is closely linked to the explainability of AI systems. The rapid advancements in AI across various domains have underscored the challenges of establishing trust, raising increasing interest in AI explainability even more when applied to deep learning. In this context, the present work aims to explore the application of explainability techniques to Reinforcement Learning (RL) algorithms, specifically within the safety-critical domain of Air Traffic Control (ATC). Using a simplified ATC environment as an initial testbed, an intelligent agent is trained with a reinforcement learning algorithm to make decisions on alternative flight routes that avoid no-fly zones. As a preliminary explainability approach, a saliency map is employed, providing insights into the input features that most significantly influence the agent's decision-making process.
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