Abstract
I argue that data becomes temporarily interesting by itself to some self-improving, but computationally limited, subjective observer once he learns to predict or compress the data in a better way, thus making it subjectively simpler and more beautiful. Curiosity is the desire to create or discover more non-random, non-arbitrary, regular data that is novel and surprising not in the traditional sense of Boltzmann and Shannon but in the sense that it allows for compression progress because its regularity was not yet known. This drive maximizes interestingness, the first derivative of subjective beauty or compressibility, that is, the steepness of the learning curve. It motivates exploring infants, pure mathematicians, composers, artists, dancers, comedians, yourself, and (since 1990) artificial systems.
First version of this preprint published 23 Dec 2008; revised April 2009. Variants are scheduled to appear as references [90] and [91] (short version), distilling some of the essential ideas in earlier work (1990-2008) on this subject: [57,58,59,60,61,68,72,76,108] and especially recent papers [81, 87, 88, 89].
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Keywords
- Recurrent Neural Network
- Human Observer
- Neural Information Processing System
- Kolmogorov Complexity
- Reinforcement Learning Algorithm
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Schmidhuber, J. (2009). Driven by Compression Progress: A Simple Principle Explains Essential Aspects of Subjective Beauty, Novelty, Surprise, Interestingness, Attention, Curiosity, Creativity, Art, Science, Music, Jokes. In: Pezzulo, G., Butz, M.V., Sigaud, O., Baldassarre, G. (eds) Anticipatory Behavior in Adaptive Learning Systems. ABiALS 2008. Lecture Notes in Computer Science(), vol 5499. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02565-5_4
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