finding aesthetic pleasure on the edge of chaos: a proposal for robotic creativity
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Finding aesthetic pleasure on the edge of chaos:A proposal for robotic creativity
Ron ChrisleyCOGSDepartment of InformaticsUniversity of Sussex
Workshop on Computational Models of Creativity in the ArtsGoldsmiths College, May 16th-17th 2006
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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
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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)
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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
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