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Creative Design Using Collaborative Interactive Genetic Algorithms
Creative Design Using Collaborative Interactive Genetic Algorithms
Creative Design Using Collaborative Interactive Genetic Algorithms
Creative Design Using Collaborative Interactive Genetic Algorithms
Creative Design Using Collaborative Interactive Genetic Algorithms
Creative Design Using Collaborative Interactive Genetic Algorithms
Creative Design Using Collaborative Interactive Genetic Algorithms
Creative Design Using Collaborative Interactive Genetic Algorithms
Creative Design Using Collaborative Interactive Genetic Algorithms
Creative Design Using Collaborative Interactive Genetic Algorithms
Creative Design Using Collaborative Interactive Genetic Algorithms
Creative Design Using Collaborative Interactive Genetic Algorithms
Creative Design Using Collaborative Interactive Genetic Algorithms
Creative Design Using Collaborative Interactive Genetic Algorithms
Creative Design Using Collaborative Interactive Genetic Algorithms
Creative Design Using Collaborative Interactive Genetic Algorithms
Creative Design Using Collaborative Interactive Genetic Algorithms
Creative Design Using Collaborative Interactive Genetic Algorithms
Creative Design Using Collaborative Interactive Genetic Algorithms
Creative Design Using Collaborative Interactive Genetic Algorithms
Creative Design Using Collaborative Interactive Genetic Algorithms
Creative Design Using Collaborative Interactive Genetic Algorithms
Creative Design Using Collaborative Interactive Genetic Algorithms
Creative Design Using Collaborative Interactive Genetic Algorithms
Creative Design Using Collaborative Interactive Genetic Algorithms
Creative Design Using Collaborative Interactive Genetic Algorithms
Creative Design Using Collaborative Interactive Genetic Algorithms
Creative Design Using Collaborative Interactive Genetic Algorithms
Creative Design Using Collaborative Interactive Genetic Algorithms
Creative Design Using Collaborative Interactive Genetic Algorithms
Creative Design Using Collaborative Interactive Genetic Algorithms
Creative Design Using Collaborative Interactive Genetic Algorithms
Creative Design Using Collaborative Interactive Genetic Algorithms
Creative Design Using Collaborative Interactive Genetic Algorithms
Creative Design Using Collaborative Interactive Genetic Algorithms
Creative Design Using Collaborative Interactive Genetic Algorithms
Creative Design Using Collaborative Interactive Genetic Algorithms
Creative Design Using Collaborative Interactive Genetic Algorithms
Creative Design Using Collaborative Interactive Genetic Algorithms
Creative Design Using Collaborative Interactive Genetic Algorithms
Creative Design Using Collaborative Interactive Genetic Algorithms
Creative Design Using Collaborative Interactive Genetic Algorithms
Creative Design Using Collaborative Interactive Genetic Algorithms
Creative Design Using Collaborative Interactive Genetic Algorithms
Creative Design Using Collaborative Interactive Genetic Algorithms
Creative Design Using Collaborative Interactive Genetic Algorithms
Creative Design Using Collaborative Interactive Genetic Algorithms
Creative Design Using Collaborative Interactive Genetic Algorithms
Creative Design Using Collaborative Interactive Genetic Algorithms
Creative Design Using Collaborative Interactive Genetic Algorithms
Creative Design Using Collaborative Interactive Genetic Algorithms
Creative Design Using Collaborative Interactive Genetic Algorithms
Creative Design Using Collaborative Interactive Genetic Algorithms
Creative Design Using Collaborative Interactive Genetic Algorithms
Creative Design Using Collaborative Interactive Genetic Algorithms
Creative Design Using Collaborative Interactive Genetic Algorithms
Creative Design Using Collaborative Interactive Genetic Algorithms
Creative Design Using Collaborative Interactive Genetic Algorithms
Creative Design Using Collaborative Interactive Genetic Algorithms
Creative Design Using Collaborative Interactive Genetic Algorithms
Creative Design Using Collaborative Interactive Genetic Algorithms
Creative Design Using Collaborative Interactive Genetic Algorithms
Creative Design Using Collaborative Interactive Genetic Algorithms
Creative Design Using Collaborative Interactive Genetic Algorithms
Creative Design Using Collaborative Interactive Genetic Algorithms
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Creative Design Using Collaborative Interactive Genetic Algorithms

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Dissertation defense. I propose a computational model of creative design based on collaborative interactive genetic algorithms. I test the computational model on two case studies: floorplanning and 3D …

Dissertation defense. I propose a computational model of creative design based on collaborative interactive genetic algorithms. I test the computational model on two case studies: floorplanning and 3D modeling.

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  • Goal-oriented, constrained decision-making activityConceptual designDetailed designEvaluation Iterative redesign
  • Alternative design concepts during this design phase may need to be subjectively evaluated, especially when requirements include aesthetics and other subjective criteria.So how do designers evaluate subjective criteria? What’s the formula, or equation that we can code into an algorithm?It is very difficult if not impossible to do so.Finally, we are also interested in supporting collaboration in designVery few times do you have a single designer working on a project, usually a team of designers works on a project, and it is increasingly common to have multidisciplinary teams working togetherSo addressing collaboration is an important aspectSo I have said that these challenges force designers to exercise their creativity to come up with solutions that meet a given set of requirements.What is creativity?So how do we propose to tackle these challenges?Change in requirementsChange in the problem understandingChange in client requirementsConflicting requirementsSubjective evaluation of alternative design conceptsAesthetics and other subjective criteriaCollaborative design
  • We find that is a very difficult question to answer, since there are many definitions of creativity.It has been argued that much of our intelligence and creativity results from interaction and collaboration with peers.
  • So this is a diagram of our model. It has a lot of information, so I’ll go over it with you.So you see three peers, each enclosed in the dotted boxes.Each peer interacts with a GA.In order to evaluate the subjective criteria, we use an interactive genetic algorithm.Explain IGA.But in our model the evaluation is not purely subjective, it has an objective component as well.The subjective criteria is the user feedback, we ask the user to pick the best from the individuals displayed on the screen.The objective criteria in this case are coded architectural guidelines, such as room sizes, dimensions, and such.The objective and subjective criteria are optimized using pareto optimality, specifically the NSGA-II, so we are not using a standard GA for the generational process.Finally, the collaboration is denoted by the arrows between the GAs.So if a peer likes something from one of the peers, then he or she can inject it into their own population, which introduces a bias.I’ll show you in a few slides what the interface actually looks like and it will make more sense.So this is our model, but a very important question is, does this model have the potential to produce creative designs?
  • A boxplot is graphic representation of numerical data depicted through its five-number summary: maximum, minimum, lower quartile, median, upper quartile, and maximum. We use a variation of the boxplot, a notched boxplot, which also shows notches around the median depicting the confidence intervals around the median. If the notches around two medians do not overlap, then it can be said that medians are statistically different at a 95% confidence level.
  • We conducted one experiment.The goal was to design a floorplan for a two-bedroom, one bath apartment, and it had to meet the following constraints.The living room should face north-west, like this example here.The two bedrooms should not have a common wall, as shown there.At least one of the bedrooms should have direct access to the bathroom.This floorplan meets all criteria.
  • We had ten computer science grad students evaluate the designs by taking a survey.I’ll discuss some more survey details in a couple of slides.The plans were evaluated for creative content based on practicality and originality on a scale from 1 to 5.
  • Transcript

    • 1. Creative Design Using Collaborative Interactive Genetic Algorithms<br />Juan C. Quiroz<br />PhD Dissertation Defense<br />Thursday April 29, 2010<br />Department of Computer Science & Engineering<br />University of Nevada, Reno<br />
    • 2. Outline<br />Creativity in Design<br />Collaborative Interactive Genetic Algorithms<br />Reducing User Fatigue in Interactive Genetic Algorithms<br />Testing Our Computational Model of Creative Design<br />Contributions<br />
    • 3. Design<br />Conceptual design<br />Detailed design<br />Evaluation <br />Iterative redesign<br />
    • 4. Conceptual Design<br />Initially conceiving and elaborating solutions that meet a set of requirements<br />Change in requirements<br />Subjective evaluation of alternative design concepts<br />Aesthetics and other subjective criteria<br />Collaboration<br />
    • 5. Creativity<br />Novel and useful<br />Role of collaboration<br />
    • 6. Computational Model of Creative Design<br />Allows for subjective exploration of solutions<br />Supports collaboration<br />Has the potential to generate creative solutions<br />
    • 7. Main Claim<br />Collaborative interactive genetic algorithms are a viable computational model of creative design<br />
    • 8. Outline<br />Creativity in Design<br />Collaborative Interactive Genetic Algorithms<br />Reducing User Fatigue in Interactive Genetic Algorithms<br />Testing Our Computational Model of Creative Design<br />Contributions<br />
    • 9. Collaborative Interactive Genetic Algorithm<br />Population based search technique<br />Natural selection<br />Survival of the fittest <br />
    • 10. CollaborativeInteractive Genetic Algorithms (IGAs)<br />Fashion design (Kim 2000)<br />Micromachine design (Kamalian 2005)<br />Music, editorial design (Takagi 2001)<br />Traveling salesman problem (Louis 1999)<br />
    • 11. Collaborative Interactive Genetic Algorithm<br />
    • 12. Creative Design<br />Floorplans with rectangular rooms<br />Purposely shifting the focus of <br /> the search space<br />Circular rooms<br />Ellipsoid rooms<br />Star-shaped rooms<br />
    • 13. Creative Design<br />
    • 14. Sharing Solutions<br />
    • 15. IGAP: Interactive Genetic Algorithm Peer to Peer<br />
    • 16. Outline<br />Creativity in Design<br />Collaborative Interactive Genetic Algorithms<br />Reducing User Fatigue in Interactive Genetic Algorithms<br />Testing Our Computational Model of Creative Design<br />Contributions<br />
    • 17. User Fatigue in Interactive Genetic Algorithms<br />Genetic Algorithms tend to rely on<br />Large populations<br />Many generations<br />Suboptimal solutions<br />Noisy fitness<br />
    • 18. Fitness Interpolation<br />Pick the best solution every nth generation<br />
    • 19. Experimental Setup<br />Test on the onemax problem<br />Subset methods<br />Best n, best n/2 and worst n/2, random n, PCA n<br />Subset size<br />Gaussian noise<br />Collaboration<br />
    • 20. Experimental Setup<br />Simulated user input<br />20 user evaluations<br />Greedy user always picks the solution with most ones<br />30 independent runs<br />Step sizes of 1, 2, 5<br />Subset size 9<br />
    • 21. Boxplots<br />Outlier<br />Maximum<br />Upper quartile<br />Median<br />Lower quartile<br />Minimum<br />Outlier<br />
    • 22. Subset Methods<br />
    • 23. Subset Size<br />Step size 1<br />Step size 2<br />Step size 5<br />
    • 24. Subset Size<br />
    • 25. No Noise vs Gaussian Noise with Sigma=1<br />Step size 1<br />Step size 2<br />Step size 5<br />
    • 26. Noise<br />
    • 27. Number of Peers<br />
    • 28. Summary<br />Users can effectively bias evolution towards high fitness solutions<br />Subset size<br />Noise<br />Collaboration<br />
    • 29. Outline<br />Creativity in Design<br />Collaborative Interactive Genetic Algorithms<br />Reducing User Fatigue in Interactive Genetic Algorithms<br />Testing Our Computational Model of Creative Design<br />Contributions<br />
    • 30. Goals<br />User studies<br />Solutions created individually<br />Solutions created collaboratively<br />Show that solutions created collaboratively are more creative<br />
    • 31. First User Study: Floorplanning<br />Living Room<br />Eating area<br />Bedroom<br />Bathroom<br />
    • 32. Collaborative Floorplanning<br />User’s Individuals<br />Peers’ Individuals<br />
    • 33. Pilot: Experimental Setup<br />Requirements<br />Design a floorplan for a 2 bedroom, 1 bathroom apartment<br />Living room should face north-west<br />The two bedrooms should not have a common wall<br />At least one of the bedrooms should have direct access to the bathroom<br />
    • 34. Pilot: Experimental Setup<br />Four colleagues and I evolved floorplans<br />Individually<br />Collaboratively<br />Ten computer science graduate students evaluated the designs by taking a survey<br />The plans were evaluated for creative content based on practicality and originality<br />
    • 35. Floorplan Results<br />
    • 36. Results<br />
    • 37. Floorplanning User Study: Experimental Setup<br />Requirements<br />Create a floorplan for a 2 bedroom, 1 bathroom apartment<br />Bathrooms close to the bedrooms<br />Bathrooms far from kitchen and dining areas<br />
    • 38. Floorplanning User Study: Experimental Setup<br />Participants:<br />8 women, 12 men<br />Five groups of size four<br />Agenda<br />Tutorial<br />Create individual floorplan<br />Create collaborative floorplan<br />Evaluation of floorplans<br />
    • 39. Evaluation Criteria<br />Appealing – unappealing<br />Average – revolutionary<br />Commonplace – original<br />Conventional – unconventional<br />Dull – exciting<br />Fresh - routine<br />Novel – predictable<br />Unique – ordinary<br />Usual - unusual<br />Meets all requirements - does not meet requirements<br />Creative Product Semantic Scale<br />Seven point Likert scale<br />
    • 40. Hypothesis<br />Is collaboration amongst peers sufficient to allow for the potential to produce creative solutions?<br />Designs evolved collaboratively will consistently rank higher in the evaluation criteria.<br />
    • 41. Results<br />
    • 42. Discussion<br />Ambiguity in evaluation criteria<br />Appealing – unappealing<br />Positive – Negative (?)<br />Negative – Positive (?)<br />Applicability of evaluation criteria<br />“Exciting”<br />Domain expert vs. student<br />Participants created only 1 collaborative floorplan and 1 individual floorplan<br />Simple graphic representation<br />
    • 43. Second User Study: 3D Modeling<br />Vertex programs<br />p.x += 20<br />p.x += sin(time)<br />
    • 44. Sample Ninja Transformations<br />
    • 45. Collaborative Setup<br />User 1<br />Equations that modify the x and z coordinates<br />User 2<br />Equations that modify the y and z coordinates<br />After collaboration<br />Equations that modify the x, y, and z coordinates<br />
    • 46. Experimental Setup<br />Design Phase<br />Two groups of 10 participants<br />Evaluation Phase<br />On-site evaluation<br />20 participants<br />Online evaluation<br />16 participants<br />
    • 47. Experimental Setup<br />Groups of 2<br />Agenda<br />Tutorial<br />Creating 3D models<br />Picking solutions for the evaluation phase<br />
    • 48. Design Phase<br />
    • 49. Evaluation Phase<br />7 point Likert scale<br />Creative Product Semantic Scale<br />The transformation is:<br />Extremely creative – Not Creative At All<br />The transformation can be used in a video game.<br />The transformation with minor tweaks can be used in a video game.<br />The transformation is novel.<br />The transformation is surprising.<br />
    • 50. Results<br />Individual<br />Collaborative<br />
    • 51. The transformation is creative.<br />
    • 52. The transformation can be used in a video game.<br />
    • 53. The transformation with minor tweaks can be used in a video game.<br />
    • 54. The transformation is novel.<br />
    • 55. The transformation is surprising.<br />
    • 56. Online Evaluation<br />Best individually created models<br />Best collaboratively created models<br />Evaluation Criteria<br />The transformation is creative.<br />The transformation can be used in a video game.<br />The transformation is novel.<br />The transformation is surprising.<br />Which of the two rows did you like the most?<br />Which of the two rows is the most creative?<br />
    • 57. Results<br />Individually created models vs collaboratively created models<br />No statistically significant results<br />
    • 58. Results<br />Which of the two rows did you like the most?<br />8 participants picked the individual row<br />7 participants picked the collaborative row<br />1 participant did not answer<br />Which of the two rows is the most creative?<br />3 participants picked the individual row<br />13 participants picked the collaborative row<br />
    • 59. Discussion<br />Different 3D models<br />Lack of context<br />Online Evaluation Nuances<br />Switching windows<br />15 second average<br />Rewinding<br />Scoring the row of individually created models first<br />Indecisive participants and median scores<br />
    • 60. Outline<br />Creativity in Design<br />Collaborative Interactive Genetic Algorithms<br />Reducing User Fatigue in Interactive Genetic Algorithms<br />Testing Our Computational Model of Creative Design<br />Contributions<br />
    • 61. Contributions<br />A new computational model of creative design<br />Subjective exploration of solutions<br />Integrates collaboration<br />Implementation of IGAP framework: Interactive Genetic Algorithm Peer to Peer<br />Analysis of our fitness interpolation technique in the onemax problem<br />
    • 62. Contributions<br />Floorplanning pilot<br />Collaborative solutions were considered more original<br />Floorplanning user study<br />Collaborative solutions were considered more original and revolutionary<br />3D Modeling user study<br />13 out of 16 participants picked row of collaborative of solutions as the most creative<br />
    • 63. Future Work<br />Conduct additional user studies<br />Long term user studies with design teams<br />Refine and test IGAP framework<br />Machine learning<br />
    • 64. Acknowledgments<br />Dr. Sushil Louis<br />Dr. Bobby Bryant<br />Dr. SwateeNaik<br />Dr. SergiuDascalu<br />Dr. AmitBanerjee<br />Dr. Darren Platt<br />Study participants<br />Students, adult volunteers, and faculty<br />This work was supported in part by contract number N00014-05-1-0709 from the Office of Naval Research and the National Science Foundation under Grant no. 0447416.<br />
    • 65. Questions?<br />Thank you!<br />www.cse.unr.edu/~quiroz<br />

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