Teaching Artificial Intelligence Good Air Traffic Flow Management

Air Traffic Flow Managers are continually faced with the decision of when and how to respond to predictions of future constraints. The promise of Artificial Intelligence, and specifically reinforcement learning, to provide decision support in this domain stems from the ability to systematically evaluate a sequence of potential actions, or strategy, across a range of uncertain futures. As decision support for human traffic managers, the generated recommendations must embody characteristics of a good management strategy; doing so requires introducing such notions to the algorithm. In this paper, we propose to induce stability into the strategy by dynamically constraining the design space to promote consistency across decisions. We further evaluate the impact of adding a performance improvement threshold that must be overcome to accept a new strategy recommendation. The combination of search constraints and threshold values is evaluated against the agent’s reward function in addition to measures proposed to capture the stability of the strategy. The results demonstrate that strategy stability can be improved without unduly impacting performance.