  In this paper, we present a novel Bayesian online prediction algorithm for
the problem setting of ad hoc teamwork under partial observability (ATPO),
which enables on-the-fly collaboration with unknown teammates performing an
unknown task without needing a pre-coordination protocol. Unlike previous works
that assume a fully observable state of the environment, ATPO accommodates
partial observability, using the agent's observations to identify which task is
being performed by the teammates. Our approach assumes neither that the
teammate's actions are visible nor an environment reward signal. We evaluate
ATPO in three domains -- two modified versions of the Pursuit domain with
partial observability and the overcooked domain. Our results show that ATPO is
effective and robust in identifying the teammate's task from a large library of
possible tasks, efficient at solving it in near-optimal time, and scalable in
adapting to increasingly larger problem sizes.
