Behavioral Learning of Aircraft Landing Sequencing Using a Society of Probabilistic Finite State Machines

Air Traffic Control (ATC) is a complex safety critical environment. A tower controller would be making many decisions in real-time to sequence aircraft. While some optimization tools exist to help the controller in some airports, even in these situations, the real sequence of the aircraft adopted by the controller is significantly different from the one proposed by the optimization algorithm. This is due to the very dynamic nature of the environment. The objective of this paper is to test the hypothesis that one can learn from the sequence adopted by the controller some strategies that can act as heuristics in decision support tools for aircraft sequencing. This aim is tested in this paper by attempting to learn sequences generated from a well-known sequencing method that is being used in the real world. The approach relies on a genetic algorithm (GA) to learn these sequences using a society Probabilistic Finite-state Machines (PFSMs). Each PFSM learns a different sub-space; thus, decomposing the learning problem into a group of agents that need to work together to learn the overall problem. Three sequence metrics (Levenshtein, Hamming and Position distances) are compared as the fitness functions in GA. As the results suggest, it is possible to learn the behavior of the algorithm/heuristic that generated the original sequence from very limited information

Mapping lessons from ants to free flight: An ant-based weather aviodance algorithm in free flight airspace

The continuing growth of air traffic worldwide motivates the need for new approaches to air traffic management that are more flexible both in terms of traffic volume and weather. Free Flight is one such approach seriously considered by the aviation community. However the benefits of Free Flight are severely curtailed in the convective weather season when weather is highly active, leading aircrafts to deviate from their optimal trajectories. This paper investigates the use of ant colony optimization in generating optimal weather avoidance trajectories in Free Flight airspace. The problem is motivated by the need to take full advantage of the airspace capacity in a Free Flight environment, while maintaining safe separation between aircrafts and hazardous weather. The experiments described herein were run on a high fidelity Free Flight air traffic simulation system which allows for a variety of constraints on the computed routes and accurate measurement of environments dynamics. This permits us to estimate the desired behavior of an aircraft, including avoidance of changing hazardous weather patterns, turn and curvature constraints, and the horizontal separation standard and required time of arrival at a pre determined point, and to analyze the performance of our algorithm in various weather scenarios. The proposed Ant Colony Optimization based weather avoidance algorithm was able to find optimum weather free routes every time if they exist. In case of highly complex scenarios the algorithm comes out with the route which requires the aircraft to fly through the weather cells with least disturbances. All the solutions generated were within flight parameters and upon integration with the flight management system of the aircraft in a Free Flight air traffic simulator, successfully negotiated the bad weather

Foundations of Trusted Autonomy

This book establishes the foundations needed to realize the ultimate goals for artificial intelligence, such as autonomy and trustworthiness. Aimed at scientists, researchers, technologists, practitioners, and students, it brings together contributions offering the basics, the challenges and the state-of-the-art on trusted autonomous systems in a single volume. The book is structured in three parts, with chapters written by eminent researchers and outstanding practitioners and users in the field. The first part covers foundational artificial intelligence technologies, while the second part covers philosophical, practical and technological perspectives on trust. Lastly, the third part presents advanced topics necessary to create future trusted autonomous systems. The book augments theory with real-world applications including cyber security, defence and space

Machine Education: Designing semantically ordered and ontologically guided modular neural networks

The literature on machine teaching, machine education, and curriculum design for machines is in its infancy with sparse papers on the topic primarily focusing on data and model engineering factors to improve machine learning. In this paper, we first discuss selected attempts to date on machine teaching and education. We then bring theories and methodologies together from human education to structure and mathematically define the core problems in lesson design for machine education and the modelling approaches required to support the steps for machine education. Last, but not least, we offer an ontology-based methodology to guide the development of lesson plans to produce transparent and explainable modular learning machines, including neural networks.Comment: IEEE Symposium Series on Computational Intelligence, 201