Simple algorithmic theory of subjective beauty, novelty, surprise, interestingness, attention, curiosity, creativity, art, science, music, jokes
Journal of SICE, 48(1):21-32.
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.
Relevant for: WP5 intrinsic/extrinsic motivations. One recent survey paper to read. Summarizes previous ideas 1990-2007. Intrinsic rewards of scientists and artists and others are essentially the same: the action selector gets rewarded for progress of the predictor = compressor = world model. This motivates the controller or experimenter to find or create patterns that exhibit formerly unknown but learnable predictability / compressibility. Unlike in the work of Birkhoff and others the important thing is to measure the \_improvements\_ of one learner (the model) to compute the intrinsic reward of another (the actor). Includes overview of the first artificial systems with intrinsic motivations since 1990.