  For an intelligent agent to be truly autonomous, it must be able to adapt its
representation to the requirements of its task as it interacts with the world.
Most current approaches to on-line feature extraction are ad hoc; in contrast,
this paper presents an algorithm that bases judgments of state compatibility
and state-space abstraction on principled criteria derived from the
psychological principle of cognitive economy. The algorithm incorporates an
active form of Q-learning, and partitions continuous state-spaces by merging
and splitting Voronoi regions. The experiments illustrate a new methodology for
testing and comparing representations by means of learning curves. Results from
the puck-on-a-hill task demonstrate the algorithm's ability to learn effective
representations, superior to those produced by some other, well-known, methods.
