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Emergent Design

Emergent Design. Martin Hemberg Imperial College 2004 Emergent Design. Who's that guy and what's he doing up there?. Martin Hemberg Developed Genr8 with the Emergent Design Group (EDG) Work at Emergent Design + Technologies (EmTech) Architectural Association (AA).

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Emergent Design

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  1. Emergent Design Martin Hemberg Imperial College 2004 Emergent Design

  2. Who's that guy and what's he doing up there? • Martin Hemberg • Developed Genr8 with the Emergent Design Group (EDG) • Work at Emergent Design + Technologies (EmTech) Architectural Association (AA) Martin Hemberg Imperial College 2004 Emergent Design

  3. Evolutionary Computation Artificial Life Emergent Design Design Agenda • Motivation • Evolutionary Computation (EC) • Artificial Life (Alife) • EC and Alife combine well for design tasks • Example applications Martin Hemberg Imperial College 2004 Emergent Design

  4. Motivation, Architecture • Break new grounds in architecture • ED uses a different logic • Natural form has aesthetic and functional values • Hard to obtain using ordinary methods Martin Hemberg Imperial College 2004 Emergent Design

  5. Motivation, Computer Science • Application of EC • Exploration, not optimisation • Fitness evaluation • Use computers creatively • Beyond CAD-tools • Requires new algorithms Martin Hemberg Imperial College 2004 Emergent Design

  6. Evolutionary Computation • Optimisation method • Randomized • Inspired by natural evolution • Population adaption generation by generation • Comes in many flavours: GA, GP, ES, GE, etc Martin Hemberg Imperial College 2004 Emergent Design

  7. Neo Darwinian Evolution • Survival of the fittest • Selection on phenotype • Through environment • Genotypic inheritance • Reproduction • Blind variation Martin Hemberg Imperial College 2004 Emergent Design

  8. Iterate by generation Fitness biased selection Inheritance Variation Artificial Evolution Pseudocode for an EA: generation = 0;initialize population;while generation < max-generation evaluate fitness of population members for 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 populationendwhile; Martin Hemberg Imperial College 2004 Emergent Design

  9. Selection • Selection ensures that fitter individuals have a higher probability of being selected for the next generation • Tournament • Proportional Martin Hemberg Imperial College 2004 Emergent Design

  10. Fitness • A leap from natural evolution • A quantified numerical value is assigned to each member • Try each member on the problem and rank them or quantify their performance Martin Hemberg Imperial College 2004 Emergent Design

  11. Fitness Evaluation • How to assign fitness according to aesthetic criteria? • EA are good at finding optimal solutions • Need to figure out what to optimize • Open problem Martin Hemberg Imperial College 2004 Emergent Design

  12. 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 • Co-evolve critics Martin Hemberg Imperial College 2004 Emergent Design

  13. Fitness Evaluation, my view • Put the user in the loop • Create tools with the designer in mind • Make them open-ended • Can't predict user's need and context • Parameterized fitness function • User has high level control of evaluation • Fitness emerges as a combination of factors Martin Hemberg Imperial College 2004 Emergent Design

  14. What is Artificial Life • 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? Martin Hemberg Imperial College 2004 Emergent Design

  15. Two definitions of emergence • The whole is greater than the sum of the parts • Emergence is the phenomenon wherein complex, interesting high-level function is produced as a result of combining simple low-level mechanisms in simple ways. • Examples include brain, society Martin Hemberg Imperial College 2004 Emergent Design

  16. The ‘fit’ is powerful and good, but not perfect Alife and EC for Design • Evolutionary computation • Creative and generative qualities • Discovery and adaptation more than optimization • ALife • Agents interacting with environment can model elements of design and conditions of the problem • Emergent properties in outcome from bottom-up approach Martin Hemberg Imperial College 2004 Emergent Design

  17. Surface Component System • Simple growth model • Select tiles from a predefined set • Rules for which tiles are allowed • Incorporates structural analysis in the EA Martin Hemberg Imperial College 2004 Emergent Design

  18. Using the tool • Implemented as a MEL script • FEA inAnsys Martin Hemberg Imperial College 2004 Emergent Design

  19. Geometric Fitness criteria • Fast and easy to evaluate and understand • Number of support points • Support point distance • Height • Holes Martin Hemberg Imperial College 2004 Emergent Design

  20. Structural Fitness criteria • FEA is computationally costly • Don't evaluate each generation Martin Hemberg Imperial College 2004 Emergent Design

  21. Genr8 – A Design Tool for Surface Generation • Combines EC and an organic growth model • Surface are grown in a reactive simulated physical environment Martin Hemberg Imperial College 2004 Emergent Design

  22. Lindenmayer Systems • Organic growth model • Widely applied to model plant growth in computer graphics • L-systems are important in formal language theory Martin Hemberg Imperial College 2004 Emergent Design

  23. Rewriting systems • A set of production rules are repeatedly applied to a seed • Rules are expressed as a grammarSeed: aRule: a->ab b->ba Martin Hemberg Imperial College 2004 Emergent Design

  24. Turtle Graphics • Turtle graphics is a way to visualize the grammar • Rules are interpreted as instructions for moving and drawing in 3D spaceSeed: aRule: a->a+a--a+aAngle: 60 Martin Hemberg Imperial College 2004 Emergent Design

  25. Plant Models • Operators “[“ (push state on stack) and “]” (pop state from stack) allows branching • Time delay • Stochasticity • Environmen (tropism)Seed: aRule: a->a[+a]aAngle: 45 Martin Hemberg Imperial College 2004 Emergent Design

  26. c b b b b a -> d[~a]b b a a a c c d b a b b d a a b d b d seed b b b b b b b b b b b b a c d b b b b b b a b b b c -> b[-~a]b c a c d b B b b b b b b c d c d -> c Map L-Systems b -> b b b Martin Hemberg Imperial College 2004 Emergent Design

  27. HEMLS • 3D • Scaling • More complex productions • Context sensitivity • Time variation • Stochastic Martin Hemberg Imperial College 2004 Emergent Design

  28. Environment • Forces • Attractors • Repellors • Gravity • Boundary Martin Hemberg Imperial College 2004 Emergent Design

  29. Evolution • Search the universe of possible surfaces • Find a grammar corresponding to the surface that the designer has in mind • Explore the universe to find interesting forms Martin Hemberg Imperial College 2004 Emergent Design

  30. Grammatical Evolution • Automatic generation of grammars • Very hard to construct by hand • 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 Martin Hemberg Imperial College 2004 Emergent Design

  31. Mappings • Genr8 contains several mappings • Increases the complexity • Individuals represented by linear genome • Selection on the phenotype that is expressed through an environment Martin Hemberg Imperial College 2004 Emergent Design

  32. Design Evaluation and Fitness • Fitness function with multiple parameters • Size • Smoothness • Soft boundary • Subdivisions • Symmetry • Undulation Martin Hemberg Imperial College 2004 Emergent Design

  33. Fitness Criteria • User determines target values and weight for the criteria • Multiparameter optimization • Trade-off between criteria • Population gives a family of solutions Martin Hemberg Imperial College 2004 Emergent Design

  34. Interruption, Intervention and Resumption (IIR) • Traditionally, EA are monolithic • User can guide the evolution by interacting and interfering • Allows for greater control • The tool cooperates with the user Martin Hemberg Imperial College 2004 Emergent Design

  35. Using Genr8 • Set up environment • Define fitness criteria and other parameters • Run a few generations • Analyze the results, adjust parameters and environment Martin Hemberg Imperial College 2004 Emergent Design

  36. More on Emergent Design • Genr8 websitehttp://www.ai.mit.edu/projects/emergentDesign/genr8/ • EDG websitehttp://web.mit.edu/arch/edg/ • EmTech website • http://www.aaschool.ac.uk/et Martin Hemberg Imperial College 2004 Emergent Design

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