Finding aesthetic pleasure on the edge of chaos: A proposal for robotic creativity Ron Chrisley COGS Department of Informatics University of Sussex Workshop on Computational Models of Creativity in the Arts Goldsmiths College, May 16th-17th 2006

Background Goal: Design a robot/environment system likely to exhibit creative behaviour: –Novel (at least for the robot) –Of (aesthetic) value (for humans, if possible) Engineering approach: –No direct modelling of human creativity –But exploit what is known about creativity in humans (and animals?), when expedient –Allow for possibility that insights into the human case may accrue anyway Manifesto only: No implementation yet –Set of "axioms" –Assume case of musical output for examples

Underlying architecture Key: Recurrent Connection (Copy) Full Inter-Connection Between Layers Of Units Action Expected Sensations Predicted State Previous Predicted State (Context Units) D-map T-map

Underlying architecture CNM: –Recurrent neural network –Forward model of environment Learns to anticipate/predict the sensory input it will receive if it performs a given action in a given context In conjunction with motivators can enable the robot to select actions that carry an expectation of "pleasure"

Main idea Add new motivators, corresponding to two dimensions of creativity: –Value –Novelty Axiom 1: If you make your robot pleasure-seeking, and make creativity pleasurable, you'll make your robot creative

Value: Appreciation Axiom 2: To be a good creator, it helps to be an appreciator –The CNM should evaluate the output of itself and others –That is, it should be able to feel pleasure upon experiencing outputs –Use this to guide its creative process (action selection)

Value: Reality Axiom 3: Let the robot experience output in the real world, as we do –Avoids the input bottleneck Robot can learn all the time Learns reality, not our edited version of it –Increases likelihood of consonance between what we value and what it values

Value: In our image Axiom 4: We won ’ t like what it likes unless it likes what we like –Built-in motivators should resemble ours –E.g., a preference for integer frequency ratios

Value: Sociability Axiom 5: An important motivator is the approval or attention of others –Indirect: Preference for human proximity/input –Direct: Buttons on robot that allow listeners to provide approval or disapproval feedback

From Saunders, 2001

Novelty: Complexity Axiom 6: Sometimes it is better not to try pursue novelty directly, but something that is correlated with it –Prefer outputs on the subjective "edge of chaos": That almost, but not quite, elude understanding of that agent at that time –Pleasure of an output is a hump-shaped function of the effort required to predict it –Result: Sing-song and white noise are boring, but catchy tunes are not

Novelty: Dynamics Axiom 7: Let dynamics play a role in appreciation –Process is temporally sensitive in several ways: 1.Pleasure associated with "getting it" depends on how much time it took to get there 2.Even if earlier portions are unpredictable (=> not pleasurable), work as a whole can be if it is coherent 3.Since the system learns, what it finds challenging, but possible, to predict (= pleasurable) will change over time

Novelty: Self-appreciation Axiom 8: Patterns in one's own states can be the objects of appreciation –Will only be a path to novelty if agent has limited access to its own processes Can only change internal states indirectly, by changing world Uses model of its processes to predict its own behaviour, rather than using those very processes themselves

Novelty: Embodiment Axiom 9: The best way to make outputs in the real world is to be embodied in the real world –Avoids the output bottleneck Robot doesn’t require intervention for it to generate and appreciate –Allows for serendipity, in the space between expected and actual outcomes –Imposes naturalness relation, making some transitions non-arbitrary (value)

Implementation issues Intended platform: –Two AIBO ERS-7s Solution: – Translate bodily movements into sound Problem: – Disembodied sound generation

Thank you! Thanks to: Maggie Boden Rob Clowes Simon Colton Jon Rowe Rob Saunders Aaron Sloman Dustin Stokes Mitchell Whitelaw for helpful comments and discussions