  Teamwork is a set of interrelated reasoning, actions and behaviors of team
members that facilitate common objectives. Teamwork theory and experiments have
resulted in a set of states and processes for team effectiveness in both
human-human and agent-agent teams. However, human-agent teaming is less well
studied because it is so new and involves asymmetry in policy and intent not
present in human teams. To optimize team performance in human-agent teaming, it
is critical that agents infer human intent and adapt their polices for smooth
coordination. Most literature in human-agent teaming builds agents referencing
a learned human model. Though these agents are guaranteed to perform well with
the learned model, they lay heavy assumptions on human policy such as
optimality and consistency, which is unlikely in many real-world scenarios. In
this paper, we propose a novel adaptive agent architecture in human-model-free
setting on a two-player cooperative game, namely Team Space Fortress (TSF).
Previous human-human team research have shown complementary policies in TSF
game and diversity in human players' skill, which encourages us to relax the
assumptions on human policy. Therefore, we discard learning human models from
human data, and instead use an adaptation strategy on a pre-trained library of
exemplar policies composed of RL algorithms or rule-based methods with minimal
assumptions of human behavior. The adaptation strategy relies on a novel
similarity metric to infer human policy and then selects the most complementary
policy in our library to maximize the team performance. The adaptive agent
architecture can be deployed in real-time and generalize to any off-the-shelf
static agents. We conducted human-agent experiments to evaluate the proposed
adaptive agent framework, and demonstrated the suboptimality, diversity, and
adaptability of human policies in human-agent teams.
