25 juin 2010 Interactive Genetic Algorithms for Creative Enhancement in UI design Dimitri Masson...
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Transcript of 25 juin 2010 Interactive Genetic Algorithms for Creative Enhancement in UI design Dimitri Masson...
25 juin 2010
Interactive Genetic Algorithmsfor Creative Enhancement in UI design
Dimitri MassonAlexandre DemeureGaelle Calvary
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Problem statement
• User Interface (UI) design is a creative process
• General question: How to support designers’ exploration of the design space?
Instantmessenger
…
…Design spaceof possible UIs
My research proposal
• Presentation of generated design solutions for the application under study
• Motivation– Compliance with designers’ practice: samples browsing– Sources of inspiration
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Outline
• State of the art
• Approach
• Prototype
• Early evaluation
• Conclusion and future work
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State of the art
• Creativity– Creative person
– Creative product
– Creative process
• Creative evolutionary systems– Interactive Genetic Algorithms (IGA)
– IGA & HCI
• UI design process– Design process
– Tools
– Model Based UI
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State of the art
• Creativity– Creative person
– Creative product
– Creative process
• Creative evolutionary systems– Interactive Genetic Algorithms (IGA)
– IGA & HCI
• UI design process– Design process
– Tools
– Model Based UI
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State of the art
• Creativity– Creative person
– Creative product
– Creative process
• Creative evolutionary systems– Interactive Genetic Algorithms (IGA)
– IGA & HCI
• UI design process– Design process
– Tools
– Model Based UI
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State of the art: Creative process
• A Darwinian process
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Variation & recombination
Selective retention
State of the art: Creative process
• A Darwinian process
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Variation & recombination
Selective retention
State of the art: Creative process
• A Darwinian process
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Variation & recombination
Selective retention
State of the art
• Creativity– Creative person
– Creative product
– Creative process
• Creative evolutionary systems– Interactive Genetic Algorithms (IGA)
– IGA & HCI
• UI design process and places of creativity– Design process
– Tools
– Model Based UI
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State of the art: IGA
Interactive Genetic Algorithms: Suitable for large problem space for which
– no analytical method can be applied– but candidate solutions can be evaluated
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Variation & recombination
Selective retention
Performed by the system Performed by the designer
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State of the art: IGA
Init a population of candidate solutions
Evaluate candidate solutions
Apply mutation and crossbreedings
Select promising candidate solutions
Design space
0.1
0.2
0.2
0.22
State of the art
• Creativity– Creative person
– Creative product
– Creative process
• Creative evolutionary systems– Interactive Genetic Algorithms (IGA)
– IGA & HCI
• UI design process– Design process
– Tools
– Model Based UI
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• Three contributions: [Monmarché 1999 & Oliver 2002] [Quiroz 2007] [Plessi 2008]– Input: one UI sample– Dimensions of the design space: fonts, colors, layout– Output: definitive UIs for the end-user not for the
designer
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State of the art: IGA & HCI
State of the art: IGA & HCI
The exploration of the design space is limited
• Evolution is limited to predefined elements
• The UI remains basically the same
Absence of high level description of the UI.
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State of the art: IGA & HCI
Absence of high level description of the UI.
As a result:
• Evolution is limited to predefined elements
• The UI remains basically the same – No widget substitution
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dd/mm/yy
State of the art: IGA & HCI
Absence of high level description of the UI.
As a result:
• Evolution is limited to predefined elements
• The UI remains basically the same – No widget substitution– No UI enrichment
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dd/mm/yy
dd/mm/yySelect a date:
State of the art: coverage
• Creativity– Creative person
– Creative product
– Creative process
• Creative evolutionary systems– Interactive Genetic Algorithms (IGA)
– IGA & HCI
• UI design process– Design process
– Tools
– Model Based UI
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State of the art: Model based UI
• High level description of the UI: concept & task model
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Transformation 1
Transformation 2
Transformation N
• Transformations
…
Outline
• State of the art
• Approach
• Prototype
• Early evaluation
• Conclusion and future works
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My approach
• Three key points:#1 Combination of IGA and Model Based UI at C&T.
#2 Evolution of transformations from C&T UI
#3 Support from a knowledge database
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Concept and task (C&T) Transformations(C&T UI)
IGA
Evolve Transformations(C&T UI)
uses
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My approach: #2 transformation
• Transformation = one tree– Edges: Select C&T elements– Nodes: Translate C&T elements into UI elements
Root of transformation
Select choices
Translate into radiobuttons
Select abstract tasks
Translate into windows
Select descendants abstract tasks
Translate into frameswith a red background
Root of the task
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My approach: #2 Genetic operations
• Mutation– Edge: changing selector– Node: Changing translation– Add/remove nodes
Select choices
Translate into radiobuttons
Select abstract tasks
Translate into windows
Select descendants abstract tasks
Translate into framesTranslate into comboboxes
Select children abstract tasks
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My approach: #2 Genetic operations
• Crossbreeding
My approach: #3 Knowledge database
• Widget representations for each task
• Attributes for each widget and manipulation function
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Choice of date
Entry textFormatBackground colorForeground color
CalendarWidthHeight
23/11/08
Outline
• State of the art
• Approach
• Prototype
• Early evaluation
• Conclusion and future works
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• VIDEO
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Prototype: Implementation
• TCL
• COMET: a widget toolkit [Demeure 2008]– Task level interactors supporting multiple presentations– Database storing design knowledge
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Outline
• State of the art
• Approach
• Prototype
• Early evaluation
• Conclusion and future works
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Early evaluation
• The purpose is to– assess the effectiveness of the approach by collecting as
many ideas and practices as possible– identify new areas for improvement
• The purpose is not to– assess the creativity improvement– evaluate the usability of the prototype
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Early evaluation: Protocol
• Protocol defined with N. Mandran, sociologist
• Face-to-face semi-structured interviews– Questionnaire about design activity
• Personal activity, Design practices, tools, etc.
– Sketch design of an Instant Messenger• e.g.: Sketches done by E1, Appendix C
– Manipulation of Magellan• Thinking aloud technique
• 11 participants: Designers, HCI experts, Students
• 2h/interview + 4h/post-analysis (> 2 weeks)
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Early evaluation: Results
I conducted an experiment to test 3 hypothesis
1. Designers find benefits from the IGA approach.– “Not perfect but interesting”.
– The process enable to reject bad UIs.
2. Designers find the evolution of widgets valuable.– Presentation of elements in the actual UI.
3. Designers find the UI enrichment valuable. – Few even notice the enrichment process.
As well as eliciting unforeseen needs about such approach.– Need for layout support.
– Need for User Interaction.
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Outline
• State of the art
• Approach
• Prototype
• Early evaluation
• Conclusion and future work
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Conclusion: My contributions
• Increment on previous approaches: IGA & MBUI– Transformation as individual representation– Widget substitution– UI enrichment
• Early inquiry of designers’ needs – Previous work focus more on the IGA than on the HCI
• Magellan
• [Masson 2010]: ACM EICS, Berlin, June 20-23
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Conclusion: Difficulties
• Large scope of the state of the art
• Automatic generation of UI is complex
• Conducting experiment is complex– Very long to set up– Require experience
• IGA requires a good modeling of individuals and fine tuning of the process
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Conclusion: Future work
• Direct edition of the samples presented – Integration of user sketches
• Support several level of fidelity
• Partial UI evaluation and evolution
• New evaluation
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Questions
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Research direction
• Presentation of generated design options.
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• A faire nb de ligne de code.
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Early evaluation: findings
• Purpose 1: Assess the effectiveness of the approach by collecting as many ideas and practices as possible
• H1: Designers find benefits from the IGA approach
– “Not perfect but interesting”.
– The process enable to reject bad UIs• H2
• H3
– identify new areas for improvement
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