1. ROB SAUNDERS, JOHN S. GERO
A CURIOUS DESIGN AGENT
A Computational Model of Novelty-Seeking Behaviour in Design
ROB SAUNDERS, JOHN S. GERO
Key Centre of Design Computing and Cognition
Department of Architectural and Design Science
The University of Sydney 2006 Australia
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2. OUTLINE A Computational Model of Curiosity
Novelty, interestingness, and curiosity.
A Simple Curious Design Agent
Behaviour and results of curious behaviour.
The Future of Curious Design Agents
Applications and future research.

3. WHY MODEL CURIOSITY? Conceptual Design  Problem Solving
Designers explore, design agents should too.
Modelling Motivations of Designers
Curiosity = internal motivation to explore.
Potential Applications to CAAD
Reducing “information overload” in CAAD.

4. A COMPUTATIONAL MODEL OF CURIOSITY

5. A COMPUTATIONAL MODEL OF CURIOSITY
Novelty Detection
On-line learning and error estimation.
Interest and Boredom
A biologically inspired model of arousal.
Curiosity
Design actions to promote further learning.

6. NOVELTY DETECTION Novelty = Classification Error
A curious agent produces a design and then compares it to prototypes learned of previous designs. The “distance” between the design and the best-matching prototype, is used as a measure of novelty. The design is then learned.
Novel patterns do not stay novel for long.

7. INTERESTINGNESS Berlyne’s Theory of Cortical Arousal The Wundt Curve
Novelty is mapped to interestingness using a non-linear function derived from Berlyne’s theory of cortical arousal. Designs are boring if they are either too familiar or too different.

8. Design Space CURIOUS BEHAVIOUR Promote Self-Directed Learning
Boring design
Given a measure of interestingness a curious agent can use it much like any other measure of “goodness” to guide selection of design actions, e.g. to change parameters inversely proportional to level of interest. The interestingness value of a similar designs decrease as a suitable prototype is learned.
Design
Space
Interesting design

9. Seeking Novel Spirograph Patterns
A CURIOUS DESIGN AGENT
Seeking Novel Spirograph Patterns

10. THE SPIROGRAPH1 Childhood Parametric Design Generator
Launched by Denys Fischer in 1965.
Dubbed ‘Toy of the Year’ in 1967.
1Spirograph is a registered trademark of Hasbro.

11. A GALLERY OF RANDOM SPIROGRAPH PATTERNS
Complex Patterns
Many rotations
Simple Patterns
Few rotations
Typical Patterns
Many more complex patterns than simple. r2 r1

12. A PROFILE OF THE CURIOUS SEARCH PROCESS
Waning Interest
Initially interesting patterns become boring.
Shifting Focus
Focus shifts to new, more interesting, patterns.

13. WHAT IS CURIOUS SEARCH FINDING?
Curious Search and Information Theory
Curious agents identify the most informative design possibilities w.r.t. the design agent’s history of experiences.
Expanding Conceptual Design Spaces
Curious agents find interesting those designs that lie just outside the current boundary of its conceptual design space.

14. CURIOUS VS. NON-CURIOUS SEARCHING
Non-Curious Agent: Random Sampling
Samples design space using random r1 & r2.
Curious Search Emphasises Novelty
~50% vs. ~15% novel prototypes learned.
An Intuitive Process of Exploration
Quickly learns some good prototypes and then concentrates on finding exceptions.

15. CURIOUS VS. NON-CURIOUS SEARCHING

16. THE FUTURE OF CURIOUS DESIGN AGENTS
Background: British Passport

17. WHY USE CURIOUS SEARCH AND EXPLORATION IN CAAD?
Utility may be Hard to Define
In non-routine design, useful measures of utility are hard to define. Curiosity provides a useful motivation to explore possibilities.
Novelty is ½ of Creativity
Creativity is often said to require novelty and utility. Most design tools can assess utility but few can assess novelty.

18. CURIOUS DESIGN AGENT RESEARCH
Computationally Model P-Creativity
Poincaré’s 4 stages of creative thought: preparation, incubation, illumination and verification.
Curious Design Assistants
Assist in the exploration of ill-defined design spaces by identifying potentially interesting background information, designs and problems.

19. CURIOUS DESIGN ASSISTANT RESEARCH
Data-Mining (Preparation)
Data-mining similar to curious search.
Filtering (Incubation/Illumination)
Reducing “information overload” by filtering out “boring” generated designs.
Fault/Novelty Detection (Verification)
Checking for unexpected faults, problems and exploiting emergence of desireable side-effects.

20. IF MICROSOFT MADE ARCHICAD…

21. Geometric Patterns and Financial Security
SEARCHING FOR NOVELTY
Geometric Patterns and Financial Security

22. THE GEOMETRIC LATHE The Geometric Lathe Anti-Forgery Device
Invented in 19th Century.
Anti-Forgery Device
Complex patterns on bank notes.
A Serious Spirograph for “Grown-ups”?
More complex arrangement of wheels.

23. THE GEOMETRIC LATHE
“[...] the Geometric Lathe has been esteemed, at all times, as the sheet anchor of public security against the dangers of forgery. […] The least change of a wheel of the eccentric, or turn of a set screw, produces a new pattern that shames the kaleidoscope. It defies the efforts of the mathematician to calculate the extent of its variations; […] and when the transfer press is brought to its aid, […] human ingenuity fails in the attempt to produce an imitation.”
Remarks on the Manufacture of Bank Notes and other Promises to Pay: Addressed to the Bankers of the Southern Confederacy (1864)

24. NOVEL GEOMETRIC PATTERNS AND BANK NOTES

25. SEARCHING FOR NOVEL SPIROGRAPH PATTERNS
Analyses Spirograph Designs as Images
Greyscale bitmap (32x32 pixels) representation.
Learns Average Spirograph Images
2D (6x6) self-organising map (SOM).
Searches Spirograph Parameter Space
Parameter space defined by 2 radii (r1 & r2).

26. THE MATHEMATICS OF SPIROGRAPHS1 2
(x, y)
x = (r1+r2)  cos1 – r2  cos2
y = (r1+r2)  sin1 – r2  sin2