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AGENCY GP: Agent-Based Genetic Programming for Spatial Exploration

AGENCY GP: Agent-Based Genetic Programming for Spatial Exploration. Peter Testa, Una-May O’Reilly Simon Greenwold Emergent Design Group, MIT mit.edu/arch/edg. Outline. Background and Motivation Genetic Programming Platform Innovations 3D Modeler Integration User Control

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AGENCY GP: Agent-Based Genetic Programming for Spatial Exploration

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  1. AGENCY GP: Agent-Based Genetic Programming for Spatial Exploration Peter Testa, Una-May O’Reilly Simon Greenwold Emergent Design Group, MIT mit.edu/arch/edg

  2. Outline • Background and Motivation • Genetic Programming • Platform Innovations • 3D Modeler Integration • User Control • Agent-based fitness • Representation and Interface • Conclusion

  3. Background and Motivation • Non-hierarchical organizations • Information technology • Increasing speed of production • New materials and techniques of manufacture

  4. Genetic Programming

  5. Platform Innovations: 3D Modeler Integration Spatial Model Agency Plug-in Alias|Wavefront Maya Extruded Maya Curves

  6. Population Individual 1 Curve 1 Parameters {Fixed} Operations {Variable} Maya Shape Curve 2 Parameters {Fixed} Operations {Variable} Maya Shape Curve 3 Parameters {Fixed} Operations {Variable} Maya Shape …

  7. Language: Operations Rotate Translate Scale Cut Boolean: Intersect Boolean: Union

  8. Platform Innovations: User Control • Starting Curves • Agents (modification of fitness function) • Interruption, Intervention, Resumption (modification of population)

  9. Interruption, Intervention, Resumption (IIR) ZWidening = 1.114 ZWidening = .8

  10. Platform Innovations: Agents • Agent-based Evaluation

  11. Representation and Interface • Data Cloud

  12. Representation and Interface • Reactive Interface Reactive Interface by Axel Kilian (MIT Thesis 2000)

  13. Conclusion • What we have • GP engine • Interpreter • IIR • Basic Agent • What we’ve shown • Where we’re going

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