Interactively Evolving User Interfaces

1. Interactively Evolving User Interfaces Juan Quiroz Committee: Dr. Sushil Louis Dr. Sergiu Dascalu Dr. Swatee Naik

2. Outline • Motivation • Related work: GAs, IGAs, UI Design • User fatigue in IGAs • UI evolution • Experiments • Results • Future Work

3. Motivation • User interface design is a complex, expensive, time consuming process • Iterative process • Users and contexts of use are numerous • Streamline and improve UI design • End-user customization

4. IGA for UI Evolution • IGA to explore the space of UIs • Creativity and insight • Evolution is guided by both the user preferences and coded guideline metrics • Pick best and worst

5. Genetic Algorithms • Population based search technique • Natural selection • Survival of the fittest

6. Interactive Genetic Algorithms (IGAs) • Fashion design (Kim 2000) • Micromachine design (Kamalian 2005) • Music, editorial design (Takagi 2001) • Traveling salesman problem (Louis 1999)

7. User Evaluation: Subjective Fitness 100 75 15 20 8 80 53 82 38

8. 1 5 6 3 2 9 8 7 4 User Evaluation: Ranking

9. User Evaluation: Tournaments A B Fitness A > Fitness B

10. User Fatigue in IGAs • GAs tend to rely on: • Large populations • Many generations • Suboptimal solutions • Noisy fitness landscapes

11. Just what I had in mind! Alleviating User Fatigue • Use small population sizes • Display a subset of population • Accelerate convergence through prediction (Llora 2005)

12. UI Design Support • GUI toolkits and libraries

13. Guidelines of Style • Microsoft, Apple, Java, KDE, Gnome • Define a common look and feel for applications • Discuss the use of color, layout of widgets, the use of fonts • Interpreting the guidelines is itself a challenge • Too vague or too specific

14. Ambiguity in Guidelines “Use color to enhance the visual impact of your widgets” – Apple’s Human Interface Guidelines

15. XUL User Interfaces • XML User Interface Language • Mark-up language for UIs • Buttons, textboxes, sliders menubars, toolbars

16. Related Work: UI Evolution • Evolution of style sheets (Monmarché et al) • Font and links color, paragraph spacing, font family, font decoration • We allow both the user and the coded guidelines to guide the evolution

17. Related Work: User Fatigue • SVMs to combat user fatigue (Llora 2005) • Kamalian et al. (2005) • User evaluation every tth generation • Demote or promote reaction to individuals • Validity constraint is used to determine viable and meaningful designs

18. Interactive Genetic Algorithms in UI Design

19. Lagoon MoveTo Panel

20. UI Representation • Two chromosomes • Widget chromosome • Layout chromosome

21. Genetic Operators for UI IGA • Single point crossover • Bit flip mutation • PMX – partial mapped crossover • Swap mutation

22. Widget Color • RGB color model • Red = (255, 0, 0), Green = (0, 255, 0), Blue = (0, 0, 255) • 224 color space for each widget • HSV • Same gamut as RGB • No significant efficiency difference in RGB and HSV

23. Fitness Evaluation • Ask the user to select the best and worst UIs from the subset displayed • Interpolate the subjective fitness of individuals in population • Compute the objective metrics taken from guidelines of style • Fitness = w1 * subjective + w2 * objective

24. 1: Fitness Evaluation Best Worst

25. Worst Best 2: Subjective Fitness Interpolation • Compare to best • Compare to worst

26. 3. Objective Fitness Computation • High contrast between widget colors and background color • Low contrast between widget colors

27. Fitness = w1 * subjective fitness + w2 * objective fitness

28. Research Questions • Which selection type is the most effective for this problem? • Who from the population do we display for user evaluation? • How often should we ask for user input?

29. Experimental Setup • Greedy simulated user • 30 independent runs, 200 generations • Population size of 100 • Roulette wheel vs. tournament • Display method comparison: best 10, random 10, best 5 and worst 5 • User input every 1, 5, 10, 20, 40, 80 generations

30. Which selection type is the most effective for this problem?

31. Who from the population do we display for user evaluation? Fitness Convergence Convergence to Blue UIs

32. How often do we ask for user input? User input every tth generation Fitness convergence Low values of t High values of t

33. How often do we ask for user input? User input every tth generation Convergence to blue UIs Low values of t High values of t

34. Experimental Setup: Actual Users • Three users • 30 generations • Pick the one they like the best and the one they like the least • 5 sessions • User input every 1, 3, 5, 10, 15 generations • 30, 10, 6, 3, and 2 user evaluations respectively

35. Results

36. What leads to the drop in average performance? • Two sessions with a user • Comparison to user selected worst turned on • Comparison to user selected worst turned off • Always pick the same UI as the best • Ask for user input every 3 generations

37. What leads to the drop in average performance?

38. Generated UIs: Simulated User

39. Generated UIs: Simulated User

40. Generated UIs: User3

41. Generated UIs: User3

42. Future Work • Ask user to select the best/worst UI only • Varying the frequency of user input during a session • Convergence acceleration with neuroevolution or SVMs • Integration with a GUI toolkit or library • User studies! • Task completion • Explore color representations • Specify the type of data that needs to be represented

43. Contributions • We can use IGAs to evolve UIs • Our simulated user and actual users are able to effectively bias the evolution of UIs • UIs reflect coded guidelines of style • Reduce user fatigue • Interpolation technique • Asking for less user input

44. Demo

45. Questions? • www.cse.unr.edu/~quiroz • quiroz@cse.unr.edu