  Active learning algorithms have been an integral part of recent advances in
artificial intelligence. However, the research in the field is widely varying
and lacks an overall organizing leans. We outline a Markovian formalism for the
field of active learning and survey the literature to demonstrate the
organizing capability of our proposed formalism. Our formalism takes a
partially observable Markovian system approach to the active learning process
as a whole. We specifically outline how querying, dataset augmentation, reward
updates, and other aspects of active learning can be viewed as a transition
between meta-states in a Markovian system, and give direction into how other
aspects of active learning can fit into our formalism.
