  Developing intelligent agents capable of seamless coordination with humans is
a critical step towards achieving artificial general intelligence. Existing
methods for human-AI coordination typically train an agent to coordinate with a
diverse set of policies or with human models fitted from real human data.
However, the massively diverse styles of human behavior present obstacles for
AI systems with constrained capacity, while high quality human data may not be
readily available in real-world scenarios. In this study, we observe that prior
to coordination, humans engage in communication to establish conventions that
specify individual roles and actions, making their coordination proceed in an
orderly manner. Building upon this observation, we propose employing the large
language model (LLM) to develop an action plan (or equivalently, a convention)
that effectively guides both human and AI. By inputting task requirements,
human preferences, the number of agents, and other pertinent information into
the LLM, it can generate a comprehensive convention that facilitates a clear
understanding of tasks and responsibilities for all parties involved.
Furthermore, we demonstrate that decomposing the convention formulation problem
into sub-problems with multiple new sessions being sequentially employed and
human feedback, will yield a more efficient coordination convention.
Experimental evaluations conducted in the Overcooked-AI environment, utilizing
a human proxy model, highlight the superior performance of our proposed method
compared to existing learning-based approaches. When coordinating with real
humans, our method achieves better alignment with human preferences and an
average performance improvement of 15% compared to the state-of-the-art.
