A Learning Classifier System Approach to Time-Critical Decision-Making in Dynamic Alternate Airport Selection

The goal of the paper is to address the need for methods to handle time-sensitive, human-centered, multi-criteria decision-making problems. In the current literature, prevalent methods rely on expressing decision-maker/stakeholder preferences through weights, ideal points, and trade-off matrices. However, these conventional approaches prove unsuitable for time-constrained, atypical, and stressful situations, such as emergencies. In such scenarios, where both time and additional factors significantly affect decision-making abilities, the effective utilization of advanced decision-making techniques becomes chal-lenging. Therefore, this paper explores the possibility of how an intelligent agent might be used to provide possible courses of action to human decision-makers/stakeholders. The agent will be put to the test to tackle the dynamic alternate airport selection problem. In emergency and time-critical situations, like an engine fire or a medical emergency, there is often a need to select an alternate airport destination dynamically midflight. During such emergencies, a lot of information must be collected and evaluated by the pilots as a basis for the decision-making process. The pilots need to compare multiple characteristics of the available airports and weigh the pros and cons of each. Given the need for clear and interpretable retroactive analysis in decision-making in general and in the aviation field in particular, the focus was placed on more interpretable and explainable models from the field of AI. Due to this, the Learning Classifier System (LCS) is to be the primary model explored. The LCS is trained on a custom dataset composed of various decision-making scenarios. The approach shows promising results and appears to merit further investigation.