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