Emergent Design

Martin Hemberg

Imperial College London

Architectural Association

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

Agenda

• Motivation• Emergence• Evolutionary

Computation (EC)• Artificial Life (ALife)

– Lindenmayer Systems (L-systems)

• Genr8

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

Motivation, Computer Science

• Application of EC– Exploration, not

optimization– Fitness evaluation

• Use computers creatively– Beyond CAD tools– Require new

algorithms and software

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

Flocking

• Boids algorithm• Craig Reynolds, 1986• Used in Lion King,

Lord of the Rings, etc• Swarm bots• Real animals

Flocking, rules

Alignment Cohesion

Separation

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

Artificial life

• Understand the principles of Biology

• How does life arise from the non-living?

• What are the potentials and limits of living systems?

• How is life related to mind, machines, and culture?

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)

Optimization

• Find the best possible solution• Mathematically:Given: a function f : A->R from some set A to the real numbersSought: an element x0 in A such that f(x0) ≤ f(x) for all x in A.

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

Genotype and phenotype

• Genotype – the genetic makeup of an organism

• Phenotype – the visible or measurable characteristics of an organism

Neo Darwinian Evolution

• Survival of the fittest• Selection on

phenotype– Through environment

• Genotypic inheritance• Reproduction• Blind variation

Artificial evolution

Pseudocode for an EA:generation = 0;

initialize population;while generation < max-generation

evaluate fitness of population membersfor i from 1 to population-size

select two parents;crossover parents -> child;mutate child;insert child into next generation’s

population;endfor;generation++;update current population

endwhile;

Iterateby generation

Fitness biased selectionInheritanceVariation

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

Selection

• Fitter individuals higher probability of selection for reproduction

• Based on phenotype, an expression of genotype

Reproduction

• Sexual vs asexual

• Recombine the existing solution candidates

• Heuristically, we know that solution will improve on average

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

Simple example

• Genome is fixed length binary string

• Fitness is equal to number of ones

– Select 1 and 2– Crossover at 2

110011000111

• New individuals110111000011

Fitness Select

4 1/3

3 ¼

3 ¼

2 1/6

Genome

110011

000111

011010

000101

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

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

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

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

Fitness Evaluation, my view

• Creative design tools with the designer central– Tools should be open-ended– Can’t predict and cater for user’s needs and

context

• Parameterized fitness function– User has high level control of evaluation

Fitness function, features

• Multiparametric optimization– Fitness emerges as a

combination of factors– Trade-off between

criteria

• Population gives family of solutions

Genr8 – A design tool for surface generation

• Combines EC and an organic growth model

• Surfaces are grown in a reactive simulated physical environment

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”

Rewrite systems

• A set of production rules are repeatedly applied to a seed

• Rules are expressed as a grammar:Seed: aRule: a->ab

b->ba

a -> ab -> abba -> abbabaab -> ....

Turtle graphics

• Turtle graphics is a way to visualize the grammar

• Rules are interpreted as instructions for moving and drawing in 3D space

Example, Koch curve

• Koch curve or snowflake, a fractal curve (infinite length but finite area)Seed: aRule: a->a+a--a+aAngle: 60

– Letter - move forward and draw line

– +/- - turn left/right

Branching

• Introduce two new operators to allow branching– “[“ – push state on stack– “]” – pop state from stack

Branching, example

Seed: aRule: a->a[+a]aAngle: 45

Turtle position

L-systems advanced features

• Additional features include– Time delay (flowers

and leaves forming)– Random growth (not

all plants identical)– Environment (tropism)

Map L-systems

a -> d[~a]b a b

d

a

b -> b b b

c -> b[-~a]b b a

bc

d -> c d c

seed

d

c

b

a

c b

b

b b

a

a

d

b b

b

b d

c a

b

b

b

b

b

b

ba

c

d

b

b

b

b

b

b

b

a

b b

bd

c

b

b

b

b

b

b

b

B b

bc

HEMLS

• 3D• Scaling

HEMLS, Environment

• Growth in a simulated reactive physical environment– Forces

• Attractors• Repellors• Gravity

– Boundaries

Growth example

HEMLS

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

HEMLS grammars

• Genr8 includes parser for HEMLS grammars

• User-specified systems– Very hard to construct by hand to obtain

specific outcome– Environmental influence very hard to predict

Pre-defined grammars

• Square and triangular patterns pre-defined– Versatile– Squares can yield

NURBS-surfaces

Evolution

• Search the universe of possible surfaces– Find a grammar

corresponding to the surface the designer has in mind

– Find something the designer was not thinking of

Grammatical Evolution

• Automatic generation of grammars

• Many constraints -> 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 O’Neill)

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

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

BNF, Example

• N = { <number>, <digit> }T = { 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 }S = { <number> }P = {<number> ::= <digit> | <number> <digit>

<digit> ::= 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 }

<number>

<digit> <number>

<digit> <number>

<digit> <number>

Example: 542 =>

5

4

2

HEMLS, BNF

• Terminals are turtle commands

• Genr8 evolves instructions for how to grow a surface

• These instructions are interpreted in a simulated environment

Mapping

• Several mappings– Increases the

complexity

• Individuals represented by linear genome

• Selection on the phenotype that is expressed through an environment

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

Fitness evaluation, example

Criteria Target Weight

Smooth 5 1

Subdivisions

0 3

Undulation 3 2

Surface Diff W Diff

7.23 2.23 2.23

0.45 0.45 1.35

6.2 3.2 6.4

Fitness value = 2.23 + 1.35 + 6.4 = 9.98

Interruption, Intervention and Resumption (IIR)

• Allow user more control of the tool

• User can guide the evolution by interacting and interfering

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

Scripting

• GUI• MEL command

– Scripts for sweeping parameter space

– Automatic saving, exporting etc

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

Using Genr8

1. Set up environment2. Decide pre-defined, user-defined or

evolved grammar– Set fitness function parameters

3. User evaluates output4. If not happy

Go back to 1else

End

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

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

– Don’t let it run and hope it will produce interesting results

– Must actively prod the tool in the desired direction

Genr8 issues

• No notion of materials or structure– User must rely on

geometry

• A few annoying bugs• Not sure how much

students really understand?

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)

Acknowledgements

• MIT– Una-May O’Reilly– Peter Testa– Simon Greenwold– Devyn Weiser

• AA– Achim Menges– Mike Weinstock– Michael Hensel– Many AA students