  We propose a new framework for building and evaluating machine learning
algorithms. We argue that many real-world problems require an agent which must
quickly learn to respond to demands, yet can continue to perform and respond to
new training throughout its useful life. We give a framework for how such
agents can be built, describe several metrics for evaluating them, and show
that subtle changes in system construction can significantly affect agent
performance.
