Abstract
In an ad hoc teamwork setting, the team needs to
coordinate their activities to perform a task without prior agreement on how to achieve it. The ad
hoc agent cannot communicate with its teammates
but it can observe their behaviour and plan accordingly. To do so, the existing approaches rely on
the teammates’ behaviour models. However, the
models may not be accurate, which can compromise teamwork. For this reason, we present Ad Hoc
Teamwork by Sub-task Inference and Selection (ATSIS) algorithm that uses a sub-task inference without relying on teammates’ models. First, the ad hoc
agent observes its teammates to infer which subtasks they are handling. Based on that, it selects its
own sub-task using a partially observable Markov
decision process that handles the uncertainty of the
sub-task inference. Last, the ad hoc agent uses the
Monte Carlo tree search to find the set of actions
to perform the sub-task. Our experiments show the
benefits of ATSIS for robust teamwork