  In 2021 the Johns Hopkins University Applied Physics Laboratory held an
internal challenge to develop artificially intelligent (AI) agents that could
excel at the collaborative card game Hanabi. Agents were evaluated on their
ability to play with human players whom the agents had never previously
encountered. This study details the development of the agent that won the
challenge by achieving a human-play average score of 16.5, outperforming the
current state-of-the-art for human-bot Hanabi scores. The winning agent's
development consisted of observing and accurately modeling the author's
decision making in Hanabi, then training with a behavioral clone of the author.
Notably, the agent discovered a human-complementary play style by first
mimicking human decision making, then exploring variations to the human-like
strategy that led to higher simulated human-bot scores. This work examines in
detail the design and implementation of this human compatible Hanabi teammate,
as well as the existence and implications of human-complementary strategies and
how they may be explored for more successful applications of AI in human
machine teams.
