Finding aesthetic pleasure
on the edge of chaos:
A proposal for robotic
creativity
Ron Chrisley
COGS
Department of Informatics
University of Sussex
Quick Time™ and a
TIFF (Uncompressed) dec ompressor
are needed to s ee this pic ture.
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
Expected
Sensations
D-map
Predicted State
T-map
Action
Key:
Full Inter-Connection Between Layers Of Units
Recurrent Connection (Copy)
Previous
Predicted
State
(Context
Units)
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
QuickTime™ and a
TIFF (LZW) decompressor
are needed to see this picture.
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
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
2.
3.
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
QuickTime™ and a
TIFF (Uncompressed) decompressor
are needed to see this picture.
• Problem:
– Disembodied sound generation
•
Solution:
– Translate bodily movements into sound
Thank you!
Thanks to:
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Maggie Boden
Rob Clowes
Simon Colton
Jon Rowe
Rob Saunders
Aaron Sloman
Dustin Stokes
Mitchell Whitelaw
for helpful comments and discussions