Steven Salzberg and David Aha Models of dynamic control tasks
are often inaccurate. Their accuracy can be improved through
recalibration, which requires an enormous amount of data. An
alternative approach improves a model by learning from
experience; in particular, using nearestneighbor and similar
memory-based reasoning algorithms to improve performance.
Recently these methods have begun to be explored by researchers
studying dynamic control tasks, with some degree of success. For
example, published demonstrations have shown how they can be
used to simulate running machines, bat balls into a bucket, and
balance poles on a moving cart. However, these demonstrations
did not highlight the fact that small changes in these learning
algorithms can dramatically alter their performance on dynamic
control tasks. We describe several variations of these
algorithms, and apply them to the problem of teaching a robot
how to catch a baseball. We empirically investigate severed
hypotheses concerning design decisions that sh