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.