1. THE DIGITAL CLOCKWORK MUSE Rob Saunders; John S. Gero Key Centre of Design Computing and Cognition The University of Sydney, NSW 2007, Australia rob@arch.usyd.edu.au; john@arch.usyd.edu.au A Computational Model of Aesthetic Evolution

2. THE CLOCKWORK MUSE “The Clockwork Muse” Colin Martindale (1990) The development of styles Literature, music, visual arts, and architecture The search for novelty Important determinant of stylistic change

3. THE LAW OF NOVELTY Outlaws repetition in word or deed Continual search for novelty Penalty worst than death – to be ignored Applied in its purest form in the arts Styles become more complex over time Expression ever more concrete and specific

4. AESTHETIC EVOLUTION Memes and Variations Spread of innovations through a social groups Collective theory formation Collaborative groups are more creative Fixed definitions of creativity Emergent definitions of what & who is creative

5. DEFINING CREATIVITY Personal definitions of creativity Creative thought processes Socio-cultural definitions of creativity Honorific notion of creative person Objective definitions of creativity Artefact is creative if novel and useful

6. INTERESTINGNESS Interestingness in discovery systems 43/242 heuristics in AM for interestingness Objective vs. subjective interestingness Data-centric vs. user-centric interestingness Unexpectedness and/or actionability Novel and/or useful (objective creativity)

7. THE DIGITAL CLOCKWORK MUSE Economy of Novelty Credit for production of novelty Curious Design Agents Self directed learning based on interestingness Genetic Artworks Evolved artworks similar to Sims (1991)

8. CURIOUS AGENTS Self-directed learners Schmidhuber (1991) Too similar  boredom Predictable subspace of artworks Too different  incomprehension Unpredictable subspace of artworks

9. A TOOL-USING CURIOUS AGENT

10. GENETIC ART This example is taken from the archive of human evolved genetic artworks kindly provided by John Mount, Scott Neal Reilly and Michael Witbrock as part of the International Interactive Genetic Art Project.

11. ARTISTIC GENETICS Tree-structured genotype Genetic programming (Koza) Mathematical expression 4D Quaternion mathematics Evaluated at every pixel Substitution of x and y variables

12. IMAGE PROCESSING 32 pixels Original imageProcessed inputs Edge detect & threshold

13. LEARNING AND NOVELTY Self-organising map Kohonen (1993) Short term memory Small volatile network Error as novelty Simple but effective

14. INTERESTINGNESS Berlyne’s theory of arousal Novelty as arousal device Pleasure/pain Reward/punishment The Wundt Curve “The Hedonic Function”

15. THE WUNDT CURVE

16. ACTIONABILITY Selection of parent artworks Most interesting artwork at current time Communication of interesting artworks Interestingness of evolved artwork Payment for interesting artworks Interestingness of received artwork

17. AN ECONOMY OF NOVELTY BORING INTERESTING Selects Evolved ImageSelects Received Image Pays Sender

18. FRACTAL DIMENSION Measure of complexity Fractional dimension between 0.0 and 2.0 Box-counting method Simple approx. to fractal dimension Empirical studies of real artists Jackson Pollock; (Taylor et al., 1999)

19. THE BOX-COUNTING METHOD 8 Count = 14 4 Count = 37 2 Count = 76 The fractal dimension is calculated by plotting the count of boxes containing detected edges against the number of boxes per side on a log-log graph and performing a linear regression. The gradient of the line produced is taken to be an estimate of the fractal dimension.

20. HEDONIC COMPLEXITY Two agents are initialised with the same prototype image but with different hedonic function favouring different levels of novelty. The fractal dimension of the images evolved by each agent was measured. The agent that sought greater novelty evolved more complex images.

21. NOVELTY VS. COMPLEXITY Repeating the same experiment for a range of agents with different hedonic functions reveals a linear relationship between the level of novelty sought and the complexity of the resulting images. Performing a linear regression on the data indicates that for every extra unit of novelty sought the fractal dimension of the evolved images rises 0.1.

22. HEDONIC COMPLEXITY N=0N=1N=2N=3N=4 N=5N=6N=7N=8N=9 N=10N=11N=12N=13N=14 N=15N=16N=17N=18N=19

23. THE LAW OF NOVELTY General (N=11) Avg. creativity = 5.57 Conservative (N=3) Ignored: “too similar” Avante Garde (N=19) Ignored: “too different”

24. NOVELTY CLIQUES Two Groups Novelty N=6, N=15 Self-reinforcing Limited payments Exceptions Agent-1 gets no credit Agent-4 pays Agent-5 one credit

25. NOVELTY CLIQUES

26. REFINING THE LAW OF NOVELTY Novelty and Complexity Extension to linear relationship Ignoring the avante garde Searching for too much novelty Self-reinforcing behaviour Payment of credit within cliques

27. FUTURE RESEARCH Investigations E.g. role of mediators between cliques Developments E.g. extend sensors, effectors and contexts Applications E.g. modelling user preferences (Baluja et al.)