  Ad hoc teamwork refers to the problem of enabling an agent to collaborate
with teammates without prior coordination. Data-driven methods represent the
state of the art in ad hoc teamwork. They use a large labeled dataset of prior
observations to model the behavior of other agent types and to determine the ad
hoc agent's behavior. These methods are computationally expensive, lack
transparency, and make it difficult to adapt to previously unseen changes,
e.g., in team composition. Our recent work introduced an architecture that
determined an ad hoc agent's behavior based on non-monotonic logical reasoning
with prior commonsense domain knowledge and predictive models of other agents'
behavior that were learned from limited examples. In this paper, we
substantially expand the architecture's capabilities to support: (a) online
selection, adaptation, and learning of the models that predict the other
agents' behavior; and (b) collaboration with teammates in the presence of
partial observability and limited communication. We illustrate and
experimentally evaluate the capabilities of our architecture in two simulated
multiagent benchmark domains for ad hoc teamwork: Fort Attack and Half Field
Offense. We show that the performance of our architecture is comparable or
better than state of the art data-driven baselines in both simple and complex
scenarios, particularly in the presence of limited training data, partial
observability, and changes in team composition.
