  Integrated space-air-ground networks promise to offer a valuable solution
space for empowering the sixth generation of communication networks (6G),
particularly in the context of connecting the unconnected and ultraconnecting
the connected. Such digital inclusion thrive makes resource management
problems, especially those accounting for load-balancing considerations, of
particular interest. The conventional model-based optimization methods,
however, often fail to meet the real-time processing and quality-of-service
needs, due to the high heterogeneity of the space-air-ground networks, and the
typical complexity of the classical algorithms. Given the premises of
artificial intelligence at automating wireless networks design and the
large-scale heterogeneity of non-terrestrial networks, this paper focuses on
showcasing the prospects of machine learning in the context of user scheduling
in integrated space-air-ground communications. The paper first overviews the
most relevant state-of-the art in the context of machine learning applications
to the resource allocation problems, with a dedicated attention to
space-air-ground networks. The paper then proposes, and shows the benefit of,
one specific use case that uses ensembling deep neural networks for optimizing
the user scheduling policies in integrated space-high altitude platform station
(HAPS)-ground networks. Finally, the paper sheds light on the challenges and
open issues that promise to spur the integration of machine learning in
space-air-ground networks, namely, online HAPS power adaptation, learning-based
channel sensing, data-driven multi-HAPSs resource management, and intelligent
flying taxis-empowered systems.
