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

TL;DR

This paper explores learning aircraft landing sequences from controllers using a society of probabilistic finite state machines, aiming to develop decision support heuristics that mimic real-world sequencing behavior.

Contribution

It introduces a novel approach combining society of PFSMs with genetic algorithms to learn aircraft sequencing strategies from limited data.

Findings

Successfully learned sequencing behavior from limited data

Compared three sequence metrics as fitness functions in GA

Demonstrated potential for decision support in ATC environments

Abstract

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.