Emergent Design

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Emergent Design

• Martin Hemberg
• Imperial College London
• Architectural Association

2
Yours Truly

• Developed Genr8 with the Emergent Design Group
  (EDG) at MIT in 2001
• Teach at the Emergent Design Technologies
  (EmTech) MA/MArch program at AA since 2003
• PhD student at Dept of Bioengineering at Imperial
  College

3
Agenda

• Motivation
• Emergence
• Evolutionary Computation (EC)
• Artificial Life (ALife)
• Lindenmayer Systems (L-systems)
• Genr8

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Motivation, Architecture

• New paradigm, based on different concepts than
  traditional design logics
• New algorithms and ways of thinking required
• Inspiration from biology
• Biomimetics
• Natural form has aesthetic and functional values

5
Motivation, Computer Science

• Application of EC
• Exploration, not optimization
• Fitness evaluation
• Use computers creatively
• Beyond CAD tools
• Require new algorithms and software

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What is Emergence?

• The whole is greater than the sum of the parts
• Bottom-up instead of top-down
• Local interactions produce global behaviour
• Examples include brain, economics, flocks, etc

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Flocking

• Boids algorithm
• Craig Reynolds, 1986
• Used in Lion King, Lord of the Rings, etc
• Swarm bots
• Real animals

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Flocking, rules
Alignment
Cohesion
Separation
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Why is emergence useful?

• Focus on bottom-up interactions
• Traditionally top-down control
• Complex behaviour comes from interaction of
  simple parts
• New possibilities for designers and architects

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Artificial life

• Understand the principles of Biology
• How does life arise from the non-living?
• Potential and feature of evolution
• What are the potentials and limits of living
  systems?
• Agent-based and emergent properties
• Flocks
• L-systems

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Evolutionary Computation

• Randomized optimization algorithm
• Inspired by natural evolution
• Comes in many different flavours,
• Genetic Algorithms (GA), bit-arrays (Holland)
• Genetic Programming (GP), executable tree
  structures (Koza)
• Evolutionary Strategies (ES), floating point
  numbers (Rechenberg and Schwefel)

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Optimization

• Find the best possible solution
• MathematicallyGiven a function f  A-gtR from
  some set A to the real numbersSought an element
  x0 in A such that f(x0) f(x) for all x in A.

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Evolutionary Computation, features

• Population of candidate solutions
• Parallel search for solutions
• Population of solutions for a specific problem
  adapts generation by generation
• No guarantees for finding global optimum

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Neo Darwinian Evolution

• Survival of the fittest
• Selection on phenotype
• Through environment
• Genotypic inheritance
• Reproduction
• Blind variation

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Genotype and phenotype

• Genotype the genetic makeup of an organism
• What is inherited
• Phenotype the visible or measurable
  characteristics of an organism

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Artificial evolution

• Pseudocode for an EA
• generation 0initialize populationwhile
  generation lt max-generation evaluate fitness of
  population members for i from 1 to
  population-size select two parents crossover
  parents -gt child mutate child insert child
  into next generations population endfor gener
  ation update current populationendwhile

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Fitness

• A leap from natural evolution
• Try each member on the problem and rank them or
  quantify their performance
• A numerical value is assigned to each member

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Selection

• Fitter individuals higher probability of
  selection for reproduction
• Based on phenotype, an expression of genotype

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Reproduction

• Sexual vs asexual
• Recombine the existing solution candidates
• Heuristically, we know that solution will improve
  on average

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Crossover and mutation

• Crossover operator mixes the genetic material
  from parents for offspring
• Recombine useful genes
• Mutation is blind variation, introduces new genes
  into the population

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Simple example

• Genome is fixed length binary string
• Fitness is equal to number of ones
• Select 1 and 2
• Crossover at 2110011000111
• New individuals
• 110111000011

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Fitness function

• Previous steps problem independent
• Choice of fitness function makes EA problem
  specific
• Defining and evaluating fitness function often
  complicated and time consuming
• Often evaluates the genome directly

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When are EAs useful?

• Good finding near-optimal solutions for
  complicated and non-linear problems
• Useful when we lack accurate representation for
  solving problem
• Requires
• Representation of candidate solutions
• Fitness function

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Fitness Evaluation

• How to assign fitness according to aesthetic
  criteria?
• How can we assign numerical values?
• Need to figure out what to optimize
• Open problem

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Fitness Evaluation, strategies

• Rule based
• Hard to define and encode rules
• Learn user preference with neural network
• Too many parameters, fails in practice
• User acts as fitness function
• Human fatigue, short runs

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Fitness Evaluation, my view

• Creative design tools with the designer central
• Tools should be open-ended
• Cant predict and cater for users needs and
  context
• Parameterized fitness function
• User has high level control of evaluation

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Fitness function, features

• Multiparametric optimization
• Fitness emerges as a combination of factors
• Trade-off between criteria
• Population gives family of solutions

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Genr8 A design tool for surface generation

• Combines EC and an organic growth model
• Surfaces are grown in a reactive simulated
  physical environment

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Lindenmayer Systems

• Organic growth model
• Widely applied to model plant growth in computer
  graphics
• L-systems are important in formal language theory
• Prusinkiewicz and Lindenmayer The algorithmic
  beauty of plants

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Rewrite systems

• A set of production rules are repeatedly applied
  to a seed
• Rules are expressed as a grammar
• Seed aRule a-gtab b-gtba
• a -gt ab -gt abba -gt abbabaab -gt ....

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Turtle graphics

• Turtle graphics is a way to visualize the grammar
• Rules are interpreted as instructions for moving
  and drawing in 3D space

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Example, Koch curve

• Koch curve or snowflake, a fractal curve
  (infinite length but finite area)
• Seed a
• Rule a-gtaa--aa
• Angle 60
• Letter - move forward and draw line
• /- - turn left/right

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Branching

• Introduce two new operators to allow branching
• push state on stack
• pop state from stack

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Branching, example

• Seed a
• Rule a-gtaaa
• Angle 45

Draw line
Turtle position
Store position on stack
Turn and draw line
Pop position from stack
Draw line
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L-systems advanced features

• Additional features include
• Time delay (flowers and leaves forming)
• Random growth (not all plants identical)
• Environment (tropism)

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Map L-systems
b -gt b
b
b
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HEMLS

• Language specifying surface growth
• 3D
• Scaling

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HEMLS, Environment

• Growth in a simulated reactive physical
  environment
• Forces
• Attractors
• Repellors
• Gravity
• Boundaries

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Growth example
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Growth example
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HEMLS

• More complex productions
• Context sensitivity
• Time variation
• Stochastic

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HEMLS rewrite systems

• Genr8 includes parser for HEMLS rewrite system
• Turtle instructions parameters
• User-specified rewrite systems
• Very hard to construct by hand to obtain specific
  outcome
• Environmental influence very hard to predict

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Pre-defined rewrite systems

• Square and triangular patterns pre-defined
• Versatile
• Squares can yieldNURBS-surfaces

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Evolution

• Search the universe of possible surfaces
• Find a rewrite system corresponding to the
  surface the designer has in mind
• Find something the designer was not thinking of

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Grammatical Evolution

• Automatic generation of grammars
• Many constraints -gt problematic for GP
• Grammatical Evolution allows any language
• Use Backus-Naur Form (BNF) to map linear genome
  into a grammar
• Genetic operations are separated from language
• www.grammatical-evolution.org (Ryan and ONeill)

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Grammars

• Form sentences, arrays of symbols or words from
  an alphabet
• A grammar defines the syntax of a language
• Formalism can be applied to English, French,
  java, algebra, etc
• I stand here correct syntax
• Here stand I incorrect syntax

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BNF

• Formal meta syntax for expressing context free
  grammars
• N - a finite set of non-terminal symbols,
• T - a finite set of terminal symbols,
• S - a special start symbol,
• P - a finite set of production rules
• Sentences can be expressed as a tree
• Leaves are terminals

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HEMLS, BNF

• Terminals are turtle commands
• Genr8 evolves instructions for how to grow a
  surface
• These instructions are interpreted in a simulated
  environment

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Mapping

• Several mappings
• Increases the complexity
• Individuals represented by linear genome
• Selection on the phenotype that is expressed
  through an environment

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Design evaluation and fitness

• Fitness function with multiple parameters
• Size
• Smoothness
• Soft boundary
• Subdivisions
• Symmetry
• Undulation
• User determines target values and weights for the
  criteria

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Fitness evaluation, example
Fitness value 2.23 1.35 6.4 9.98
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Interruption, Intervention and Resumption (IIR)

• Allow user more control of the tool
• User can guide the evolution by interacting and
  interfering

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Practicalities

• Plug-in for Maya
• Advantages for user
• Easy to integrate into a design process
• Easier to learn
• Advantages for developer
• Lots of functionality for free

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Scripting

• GUI
• MEL command
• Scripts for sweeping parameter space
• Automatic saving, exporting etc

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When and why to use Genr8

• Digital sketching tool
• Can provide suggestions to the user
• Prepare to give up control
• Create using different logic
• Explore novel algorithms for form-finding

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Using Genr8

• Set up environment
• Decide pre-defined, user-defined or evolved
  grammar
• Set fitness function parameters
• User evaluates output
• If not happy Go back to 1else End

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To keep in mind

• Understand difference between growth and
  evolution
• Understand difference in impact between
  parameters, environment and fitness criteria
• Not all parameters are equally important
• Pre-defined grammars environment very powerful

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To keep in mind

• Avoid getting overwhelmed by the volume of output
• Impose external evaluation criteria which is
  mapped into Genr8, possibly via external analysis
• Dont let it run and hope it will produce
  interesting results
• Must actively prod the tool in the desired
  direction

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Genr8 issues

• No notion of materials or structure
• User must rely on geometry
• A few annoying bugs
• Not sure how much students really understand?

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More about emergent design

• Genr8 website
• http//projects.csail.mit.edu/emergentDesign/genr8
  
• EDG website
• http//web.mit.edu/arch/edg
• EmTech website
• http//www.aaschool.ac.uk/et
• Special issue of Architectural Design (AD)

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Acknowledgements

• MIT
• Una-May OReilly
• Peter Testa
• Simon Greenwold
• Devyn Weiser
• AA
• Achim Menges
• Mike Weinstock
• Michael Hensel
• Katrin Jonas
• Michel da Costa Goncalves
• Steve Fuchs
• many AA students

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BNF, Example

• N ltnumbergt, ltdigitgt T 0, 1, 2, 3, 4, 5,
  6, 7, 8, 9 S ltnumbergt P ltnumbergt
  ltdigitgt ltnumbergt ltdigitgt ltdigitgt 0 1
  2 3 4 5 6 7 8 9

Example 542 gt
ltnumbergt
ltdigitgt
ltnumbergt
5
ltdigitgt
ltnumbergt
4
ltdigitgt
ltnumbergt
2