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

Assisting Unknown Teammates in Unknown Tasks: Ad Hoc Teamwork under Partial Observability