  I postulate that human or other intelligent agents function or should
function as follows. They store all sensory observations as they come - the
data is holy. At any time, given some agent's current coding capabilities, part
of the data is compressible by a short and hopefully fast program / description
/ explanation / world model. In the agent's subjective eyes, such data is more
regular and more "beautiful" than other data. It is well-known that knowledge
of regularity and repeatability may improve the agent's ability to plan actions
leading to external rewards. In absence of such rewards, however, known beauty
is boring. Then "interestingness" becomes the first derivative of subjective
beauty: as the learning agent improves its compression algorithm, formerly
apparently random data parts become subjectively more regular and beautiful.
Such progress in compressibility is measured and maximized by the curiosity
drive: create action sequences that extend the observation history and yield
previously unknown / unpredictable but quickly learnable algorithmic
regularity. We discuss how all of the above can be naturally implemented on
computers, through an extension of passive unsupervised learning to the case of
active data selection: we reward a general reinforcement learner (with access
to the adaptive compressor) for actions that improve the subjective
compressibility of the growing data. An unusually large breakthrough in
compressibility deserves the name "discovery". The "creativity" of artists,
dancers, musicians, pure mathematicians can be viewed as a by-product of this
principle. Several qualitative examples support this hypothesis.
