  We present an architecture for ad hoc teamwork, which refers to collaboration
in a team of agents without prior coordination. State of the art methods for
this problem often include a data-driven component that uses a long history of
prior observations to model the behaviour of other agents (or agent types) and
to determine the ad hoc agent's behaviour. In many practical domains, it is
challenging to find large training datasets, and necessary to understand and
incrementally extend the existing models to account for changes in team
composition or domain attributes. Our architecture combines the principles of
knowledge-based and data-driven reasoning and learning. Specifically, we enable
an ad hoc agent to perform non-monotonic logical reasoning with prior
commonsense domain knowledge and incrementally-updated simple predictive models
of other agents' behaviour. We use the benchmark simulated multi-agent
collaboration domain Fort Attack to demonstrate that our architecture supports
adaptation to unforeseen changes, incremental learning and revision of models
of other agents' behaviour from limited samples, transparency in the ad hoc
agent's decision making, and better performance than a data-driven baseline.
