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

Simple Algorithmic Principles of Discovery, Subjective Beauty, Selective Attention, Curiosity & Creativity