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Finding aesthetic pleasure on the edge of chaos: A proposal for robotic creativity

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.

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Finding aesthetic pleasure on the edge of chaos: A proposal for robotic creativity

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  1. 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

  2. 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

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

  4. 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"

  5. 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

  6. 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)

  7. 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

  8. 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

  9. 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

  10. From Saunders, 2001

  11. 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

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

  13. 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

  14. 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)

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

  16. 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

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