There has been extensive research in formalising air traffic complexity, but existing works focus mainly on a metric to tie down the peak air traffic controllers workload rather than a dynamic approach to complexity that could guide both strategical, pre-tactical and tactical actions for a smooth flow of aircraft. In this paper, aircraft interdependencies are formalized using graph theory and four complexity indicators are described, which combine spatiotemporal topological information with the severity of the interdependencies. These indicators can be used to predict the dynamic evolution of complexity, by not giving one single score, but measuring complexity in a time window. Results show that these indicators can capture complex spatiotemporal areas in a sector and give a detailed and nuanced view of sector complexity.
Safety is the primary concern when it comes to air traffic. In-flight safety between Unmanned Aircraft Vehicles (UAVs) is ensured through pairwise separation minima, utilizing conflict detection and resolution methods. Existing methods mainly deal with pairwise conflicts, however, due to an expected increase in traffic density, encounters with more than two UAVs are likely to happen. In this paper, we model multi-UAV conflict resolution as a multiagent reinforcement learning problem. We implement an algorithm based on graph neural networks where cooperative agents can communicate to jointly generate resolution maneuvers. The model is evaluated in scenarios with 3 and 4 present agents. Results show that agents are able to successfully solve the multi-UAV conflicts through a cooperative strategy.
Personal values represent what people find important in their lives, and are key drivers of human behavior. For this reason, support agents should provide help that is aligned with the personal values of the users. To do this, the support agent not only should know the value preferences of the user, but also how different situations in the user’s life affect these personal values. We represent situations using their psychological characteristics, and we build predictive models that given the psychological characteristics of a situation, predict whether the situation promotes, demotes or does not affect a personal value. In this work, we focus on predictions for the value ‘enjoyment of life’, and use different machine learning classifiers, all of them performing better than chance when training on data from multiple people. The best predictive model is a multi-layer perceptron classifier, which achieves an accuracy of 72%. Further, we hypothesize that the accuracy of such models would drop when tested on individual data sets. The data supports our hypothesis, and the accuracy of the best performing model drops by at least 11% when tested on individual data. To tackle this, we propose an active learning procedure to build personalized prediction models having the user in the loop. Results show that this approach outperforms the previously built model while using only 30% of the training data. Our findings suggest that how situations affect personal values can have subjective interpretations, but we can account for those subjective interpretations by involving the user when building a prediction model.
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