  Future warfare will require Command and Control (C2) personnel to make
decisions at shrinking timescales in complex and potentially ill-defined
situations. Given the need for robust decision-making processes and
decision-support tools, integration of artificial and human intelligence holds
the potential to revolutionize the C2 operations process to ensure adaptability
and efficiency in rapidly changing operational environments. We propose to
leverage recent promising breakthroughs in interactive machine learning, in
which humans can cooperate with machine learning algorithms to guide machine
learning algorithm behavior. This paper identifies several gaps in
state-of-the-art science and technology that future work should address to
extend these approaches to function in complex C2 contexts. In particular, we
describe three research focus areas that together, aim to enable scalable
interactive machine learning (SIML): 1) developing human-AI interaction
algorithms to enable planning in complex, dynamic situations; 2) fostering
resilient human-AI teams through optimizing roles, configurations, and trust;
and 3) scaling algorithms and human-AI teams for flexibility across a range of
potential contexts and situations.
