  In this work, we develop a game-theoretic modeling of the interaction between
a human operator and an autonomous decision aid when they collaborate in a
multi-agent task allocation setting. In this setting, we propose a decision aid
that is designed to calibrate the operator's reliance on the aid through a
sequence of interactions to improve overall human-autonomy team performance.
The autonomous decision aid employs a long short-term memory (LSTM) neural
network for human action prediction and a Bayesian parameter filtering method
to improve future interactions, resulting in an aid that can adapt to the
dynamics of human reliance. The proposed method is then tested against a large
set of simulated human operators from the choice prediction competition (CPC18)
data set, and shown to significantly improve human-autonomy interactions when
compared to a myopic decision aid that only suggests predicted human actions
without an understanding of reliance.
