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An efficient design of a Multi-Objective LearningClassifier System for multi-flight navigation is presented. A classifier is represented by a set of rules, which are used to simultaneously navigate all the flights in the airspace. Navigation of a flight is based on the relation of the flight with factors of the air traffic environment such as wind, storm as well as other flights. This system continually learns and refines the rules of classifiers by a multi-objective optimization algorithm -NSGAII -to discover the trade-off set of classifiers which navigate flights without any conflict, minimal distance of flying, minimal discomfort defined by storm level and the time duration of flights passing through storm areas, and minimizing total delay time of flights.We propose to detect conflicts between flights by grouping trajectory segments in 3-D (abscissa-x, ordinate-y, and timet) boxes. The conflict detection is only implemented in a box, thus the number of conflict detection times approximates to the number of conflicts. Further, conflicts between flights are resolved using a hill climber by propagating delays in the takeoff time of conflicting flights. The advantage of the proposed system is that the classifier outputs its rules in a symbolic representation, making the overall process transparent to the user and reusable. Moreover, the system successfully discovered rules in all runs to optimize its performance.

With the increase in automation to serve the growing needs and challenges of aviation, air traffic controllers (ATCs) are now faced with an information overload from a myriad of sources, both in graphical and textual format. One such source is weather information, which is typically comprised of wind speed, wind direction, thunderstorms, cloud cover, icing, temperature and pressure at various altitudes. This information requires domain expertise to interpret and communicate to ATCs, who then employ this information to manage air traffic efficiently and safely. Unfortunately, ATCs are not trained meteorologists, so there are significant challenges associated with the correct interpretation and utilization of this information by ATCs. In this paper, we propose a bio-inspired weather robot, which interacts with the air traffic environment and provides targeted weather-related information to ATCs by identifying which airspace sectors they are working on. It uses bio-inspired techniques for processing weather information and path planning in the air traffic environment and is fully autonomous in the sense that it only interacts with the air traffic environment passively and has an onboard weather information processing system. The weather robot system was evaluated in an experimental environment with live Australian air traffic, where it successfully navigated the environment, processed weather information, identified airspace sectors and delivered weather-related information for the relevant sector using a synthetic voice.