NDLI: Behavioral learning of aircraft landing sequencing using a society of Probabilistic Finite state Machines

Content Provider	IEEE Xplore Digital Library
Author	Jiangjun Tang Abbass, H.A.
Copyright Year	2014
Description	Author affiliation: Sch. of Eng. & Inf. Technol., UNSWSW-Canberra, Canberra, ACT, Australia (Jiangjun Tang; Abbass, H.A.)
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.
Starting Page	610
Ending Page	617
File Size	1589001
Page Count	8
File Format	PDF
e-ISBN	9781479914883
DOI	10.1109/CEC.2014.6900597
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-07-06
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Aircraft Measurement Sequential analysis Aircraft manufacture Biological cells Probabilistic logic Automata
Content Type	Text
Resource Type	Article

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