Air Traffic Controller Workload Level Prediction using Conformalized Dynamical Graph Learning

Air traffic control (ATC) is a safety-critical service system that demands constant attention from ground air traffic controllers (ATCos) to maintain daily aviation operations. The workload of the ATCos can have negative effects on operational safety and airspace usage. To avoid overloading and ensure an acceptable workload level for the ATCos, it is important to predict the ATCos' workload accurately for mitigation actions. In this paper, we first perform a review of research on ATCo workload, mostly from the air traffic perspective. Then, we briefly introduce the setup of the human-in-the-loop (HITL) simulations with retired ATCos, where the air traffic data and workload labels are obtained. The simulations are conducted under three Phoenix approach scenarios while the human ATCos are requested to self-evaluate their workload ratings (i.e., low-1 to high-7). Preliminary data analysis is conducted. Next, we propose a graph-based deep-learning framework with conformal prediction to identify the ATCo workload levels. The number of aircraft under the controller's control varies both spatially and temporally, resulting in dynamically evolving graphs. The experiment results suggest that (a) besides the traffic density feature, the traffic conflict feature contributes to the workload prediction capabilities (i.e., minimum horizontal/vertical separation distance); (b) directly learning from the spatiotemporal graph layout of airspace with graph neural network can achieve higher prediction accuracy, compare to hand-crafted traffic complexity features; (c) conformal prediction is a valuable tool to further boost model prediction accuracy, resulting a range of predicted workload labels. The code used is available at \href{https://github.com/ymlasu/para-atm-collection/blob/master/air-traffic-prediction/ATC-Workload-Prediction/}{$\mathsf{Link}$}

Reinforcement Learning With Reward Machines in Stochastic Games

We investigate multi-agent reinforcement learning for stochastic games with complex tasks, where the reward functions are non-Markovian. We utilize reward machines to incorporate high-level knowledge of complex tasks. We develop an algorithm called Q-learning with reward machines for stochastic games (QRM-SG), to learn the best-response strategy at Nash equilibrium for each agent. In QRM-SG, we define the Q-function at a Nash equilibrium in augmented state space. The augmented state space integrates the state of the stochastic game and the state of reward machines. Each agent learns the Q-functions of all agents in the system. We prove that Q-functions learned in QRM-SG converge to the Q-functions at a Nash equilibrium if the stage game at each time step during learning has a global optimum point or a saddle point, and the agents update Q-functions based on the best-response strategy at this point. We use the Lemke-Howson method to derive the best-response strategy given current Q-functions. The three case studies show that QRM-SG can learn the best-response strategies effectively. QRM-SG learns the best-response strategies after around 7500 episodes in Case Study I, 1000 episodes in Case Study II, and 1500 episodes in Case Study III, while baseline methods such as Nash Q-learning and MADDPG fail to converge to the Nash equilibrium in all three case studies

Strontium-Doped Low-Temperature-Processed CsPbI₂Br Perovskite Solar Cells

Cesium (Cs) metal halide perovskites for photovoltaics have gained research interest due to their better thermal stability compared to their organic–inorganic counterparts. However, demonstration of highly efficient Cs-based perovskite solar cells requires high annealing temperature, which limits their use in multijunction devices. In this work, low-temperature-processed cesium lead (Pb) halide perovskite solar cells are demonstrated. We have also successfully incorporated the less toxic strontium (Sr) at a low concentration that partially substitutes Pb in CsPb_1–<i>x</i>Sr_<i>x</i>I₂Br. The crystallinity, morphology, absorption, photoluminescence, and elemental composition of this low-temperature-processed CsPb_1–<i>x</i>Sr_<i>x</i>I₂Br are studied. It is found that the surface of the perovskite film is enriched with Sr, providing a passivating effect. At the optimal concentration (<i>x</i> = 0.02), a mesoscopic perovskite solar cell using CsPb_0.98Sr_0.02I₂Br achieves a stabilized efficiency at 10.8%. This work shows the potential of inorganic perovskite, stimulating further development of this material