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a CAPpella Prototyping Context-Aware Applications by Demonstration Ian Li CSE, University of Washington SUPERB 2003 Advised by Prof. Anind Dey Building Context-Aware Applications By writing code Context Fabric (Hong et. al., 2001) Context Toolkit (Dey, 1999) For developers, not for non-programmers By visual programming iCAP (interactive Context-Aware Application Prototyper) (Sohn & Dey, 2003) Visual language to create rules Machine Learning Classical machine- learning (CML) model Train Classify Feature Selection Interactive Use Interactive machine learning (IML) model Train Classify Feature Selection Interactive Use Manual Correction Feedback to Designer Feature Selection a CAPpella Tool for building context-aware applications by demonstration Uses IML model Event views Action view Player panel Classificat ion table How to use 1. Start system and demonstrate. 2. Stop system; system stores events in logs. 3. Use interface to view and select relevant events. 4. Feed relevant events to learning algorithm. 5. Go back to step 1 or 3. Viewing logs of events Boolean-valued events Integer-valued events Events w/ double- precision values Events w/ multiple points RFID events Actions Provides playback of raw data (e.g., video) from demonstration Programming-by- Demonstration Allow users to “act out” what the context-aware application should do Example: programming a context-aware application to turn on the lights in a room Two things to do this: Machine-learning system User interface design Assumes training is done once, so it is slow Selecting features is hard Learning algorithm does both feature selection and learning User corrects mistakes of learning algorithm (Fails & Olson, 2003) Paper Prototype Test 1.Horizontal or vertical view? 2.Visualization: cells strips dots Time series of number of people in a room 3.Select transitions or intervals An interval A transition 1.Preferred vertical view, but… 3.No preference for transitions or intervals 2.Preferred strips version, but cells delineated events + = Solution: Problem: value in line graph goes from left-to-right instead of up-and-down. Display for sound level Solution: implementation is horizontal Questions Results Future Work Evaluate user interface Integrate user interface and machine-learning system Consider order of events Ex.: Door bell rings must come before person coming in Improve visualization of classification (color code?) Allows global and detailed view of events with zoom slider Selecting relevant events User Interface Gives feedback about the classifications of a demonstration Acknowledgements Selecting individual events Selecting multiple events 1. Go inside room. 2. Turn on the lights. 3. Repeat several times, until the application learns that every time you go inside the room, it should turn on the lights. Prof. Anind Dey, Jason, Raffay, Holly, Gary, Scott K., Jimmy, Prof. Jen Mankoff, Bola, and SUPERB Program

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Paper Prototype Test. Preferred vertical view, but…. Questions. Display for sound level. Problem: value in line graph goes from left-to-right instead of up-and-down. Solution: implementation is horizontal. Preferred strips version, but cells delineated events. Solution:. - PowerPoint PPT Presentation

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Page 1: a CAPpella

a CAPpellaPrototyping Context-Aware Applications by Demonstration

Ian LiCSE, University of Washington

SUPERB 2003

Advised by Prof. Anind Dey

Building Context-AwareApplications

•By writing code•Context Fabric (Hong et. al.,

2001) Context Toolkit (Dey, 1999)

•For developers, not for non-programmers

•By visual programming•iCAP (interactive Context-Aware Application Prototyper) (Sohn & Dey, 2003)

•Visual language to create rules

Machine Learning

•Classical machine-learning (CML) model

Train ClassifyFeature

Selection

Interactive Use

•Interactive machine learning (IML) model

Train ClassifyFeature

Selection

Interactive Use

ManualCorrection

Feedback toDesigner

FeatureSelection

a CAPpella

•Tool for building context-aware applications by demonstration

•Uses IML model

Event views

Actionview

Playerpanel

Classificationtable

How to use1. Start system and demonstrate.2. Stop system; system stores events in

logs.3. Use interface to view and select relevant

events.4. Feed relevant events to learning

algorithm.5. Go back to step 1 or 3.

•Viewing logs of eventsBoolean-valued

events

Integer-valued events

Events w/ double-precision values

Events w/ multiple points

RFID events

Actions

•Provides playback of raw data (e.g., video) from demonstration

Programming-by-Demonstration

•Allow users to “act out” what the context-aware application should do

•Example: programming a context-aware application to turn on the lights in a room

•Two things to do this:•Machine-learning system•User interface design

•Assumes training is done once, so it is slow

•Selecting features is hard

•Learning algorithm does both feature selection and learning

•User corrects mistakes of learning algorithm

(Fails & Olson, 2003)

Paper Prototype Test

1.Horizontal or vertical view?2.Visualization:

cells

strips

dotsTime series of number of people in a room

3.Select transitions or intervalsAn interval A transition

1.Preferred vertical view, but…

3.No preference for transitions or intervals

2.Preferred strips version, but cells delineated events

+ =

Solution:

Problem: value in line graph goes from left-to-right instead of up-and-down.

Display for sound level

Solution: implementation is horizontal

Questions

Results

Future Work•Evaluate user interface•Integrate user interface and machine-learning system

•Consider order of events•Ex.: Door bell rings must come before person coming in

•Improve visualization of classification (color code?)

•Allows global and detailed view of events with zoom slider

•Selecting relevant events

User Interface

•Gives feedback about the classifications of a demonstration

Acknowledgements

Selecting individual events

Selecting multiple events

1. Go inside room.2. Turn on the lights.3. Repeat several times, until the

application learns that every time you go inside the room, it should turn on the lights.

•Prof. Anind Dey, Jason, Raffay, Holly, Gary, Scott K., Jimmy, Prof. Jen Mankoff, Bola, and SUPERB Program