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Taking over routine tasks: Intelligent interfaces for e-mail
Taking over routine tasks: Intelligent interfaces for e-mail
Taking over routine tasks: Intelligent interfaces for e-mail
Taking over routine tasks: Intelligent interfaces for e-mail
Taking over routine tasks: Intelligent interfaces for e-mail
Taking over routine tasks: Intelligent interfaces for e-mail
Taking over routine tasks: Intelligent interfaces for e-mail
Taking over routine tasks: Intelligent interfaces for e-mail
Taking over routine tasks: Intelligent interfaces for e-mail
Taking over routine tasks: Intelligent interfaces for e-mail
Taking over routine tasks: Intelligent interfaces for e-mail
Taking over routine tasks: Intelligent interfaces for e-mail
Taking over routine tasks: Intelligent interfaces for e-mail
Taking over routine tasks: Intelligent interfaces for e-mail
Taking over routine tasks: Intelligent interfaces for e-mail
Taking over routine tasks: Intelligent interfaces for e-mail
Taking over routine tasks: Intelligent interfaces for e-mail
Taking over routine tasks: Intelligent interfaces for e-mail
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Taking over routine tasks: Intelligent interfaces for e-mail

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A summary of two intelligent interfaces for e-mail, used as a starting point for class discussion. …

A summary of two intelligent interfaces for e-mail, used as a starting point for class discussion.

Presented on Oct. 16, 2007 for CPSC 532B (http://www.cs.ubc.ca/~conati/532b-2007/532-description.html)

Published in: Business, Technology
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  • 1. Taking over routine tasks Intelligent user interfaces for e-mail Lucas Rizoli 2007-10-16 CPSC 532C
  • 2. <ul><ul><li>Segal & Kephart, 1999: </li></ul></ul><ul><ul><li>MailCat: An Intelligent Assistant for Organizing E-Mail </li></ul></ul><ul><ul><li>Segal & Kephart, 2000: </li></ul></ul><ul><ul><li>Incremental Learning in SwiftFile </li></ul></ul><ul><ul><li>Horvitz, 1999: </li></ul></ul><ul><ul><li>Principles of Mixed-Initiative User Interfaces </li></ul></ul>
  • 3. SwiftFile (aka. MailCat ) (Segal & Kephart, 1999 and 2000)
  • 4. <ul><ul><li>Facilitates message filing </li></ul></ul><ul><ul><ul><li>Given a message, predicts folder </li></ul></ul></ul><ul><ul><ul><li>Adds shortcuts to 3 likeliest folders </li></ul></ul></ul>
  • 5. <ul><ul><li>Segal & Kephart’s goals: </li></ul></ul><ul><ul><ul><li>“ Substantial benefit to users” </li></ul></ul></ul><ul><ul><ul><li>“ Users... not required to learn” </li></ul></ul></ul><ul><ul><ul><li>“ Errors... have no negative impact ” </li></ul></ul></ul><ul><ul><ul><li>“ User [is] able to ignore it” </li></ul></ul></ul><ul><ul><ul><li>“ Incremental learning to adapt [to user] ” </li></ul></ul></ul>
  • 6. <ul><ul><li>Folders predicted using TF-IDF </li></ul></ul><ul><ul><ul><li>Messages vectors of word frequencies </li></ul></ul></ul><ul><ul><ul><li>Folders sum of vectors in folder </li></ul></ul></ul><ul><ul><ul><li>Distance variation of cos distance, SIM4 </li></ul></ul></ul><ul><ul><ul><li>Closer to folder, likelier to be filed there </li></ul></ul></ul>
  • 7. <ul><ul><li>1 </li></ul></ul><ul><ul><li>98 </li></ul></ul><ul><ul><li>1370 </li></ul></ul><ul><ul><li>2 </li></ul></ul><ul><ul><li>72 </li></ul></ul><ul><ul><li>2415 </li></ul></ul><ul><ul><li>5 </li></ul></ul><ul><ul><li>19 </li></ul></ul><ul><ul><li>7411 </li></ul></ul><ul><ul><li>user # </li></ul></ul><ul><ul><li>folders </li></ul></ul><ul><ul><li>messages </li></ul></ul><ul><ul><li># buttons </li></ul></ul><ul><ul><li>accuracy </li></ul></ul><ul><ul><li>TF-IDF Prediction </li></ul></ul><ul><ul><li>Most frequent folders </li></ul></ul>
  • 8. <ul><ul><li>Accuracy with growth and change in mail </li></ul></ul><ul><ul><ul><li>Calculated using a moving average </li></ul></ul></ul><ul><ul><li># messages </li></ul></ul><ul><ul><li>accuracy </li></ul></ul>
  • 9. User Model Upward Inference Input User Folders, Filing TF-IDF Calculations Frequency vectors Downward Inference Output MoveTo Buttons Distance calculations
  • 10. LookOut (Horvitz, 1999)
  • 11. <ul><ul><li>Nearly automates scheduling </li></ul></ul><ul><ul><ul><li>Finds scheduling messages using SVM </li></ul></ul></ul><ul><ul><ul><li>Adds appointment in calendar </li></ul></ul></ul><ul><ul><ul><ul><li>Notes conflicts </li></ul></ul></ul></ul><ul><ul><ul><ul><li>If unsure, opens calendar to week </li></ul></ul></ul></ul><ul><ul><li>Manual, automatic, social-agent </li></ul></ul><ul><ul><ul><li>Dialogue, speech recognition </li></ul></ul></ul>
  • 12. <ul><ul><li>Horvitz’s factors: </li></ul></ul><ul><ul><ul><li>Value-added </li></ul></ul></ul><ul><ul><ul><li>Minimizing costs of errors </li></ul></ul></ul><ul><ul><ul><li>Aware of user’s attention </li></ul></ul></ul><ul><ul><ul><li>User can start/stop system </li></ul></ul></ul><ul><ul><ul><li>Socially appropriate behaviour </li></ul></ul></ul><ul><ul><ul><li>... </li></ul></ul></ul>“ Substantial benefit ” “ Errors... have no negative impact ” “ User [is] able to ignore ”
  • 13. <ul><ul><ul><li>Uncertainty about goals </li></ul></ul></ul><ul><ul><ul><li>Best solution given constraints </li></ul></ul></ul><ul><ul><ul><li>Match actions to certainty </li></ul></ul></ul><ul><ul><ul><li>User and agent refine results </li></ul></ul></ul><ul><ul><ul><li>Dialogue to resolve uncertainties </li></ul></ul></ul><ul><ul><ul><li>History of actions </li></ul></ul></ul><ul><ul><ul><li>Continued learning </li></ul></ul></ul>“ Incremental learning ”
  • 14. <ul><ul><li>Evidence-based decision model </li></ul></ul><ul><ul><li>Nothing </li></ul></ul><ul><ul><li>Dialogue </li></ul></ul><ul><ul><li>Act </li></ul></ul>
  • 15. <ul><ul><li>Time-based model of user attention </li></ul></ul><ul><ul><li>message size (bytes) </li></ul></ul><ul><ul><li>time before action (sec) </li></ul></ul>
  • 16. User Model Upward Inference Input User Messages, Settings, Responses Messages thru SVM Attention model, Utilities, SVM Downward Inference Output Scheduled appt., Cue for dialogue Decision function
  • 17. Discussion <ul><li>Where did the principles come from? </li></ul><ul><ul><li>LookOut </li></ul></ul><ul><ul><ul><li>How are utilities found? </li></ul></ul></ul><ul><ul><ul><li>User-set thresholds useful? </li></ul></ul></ul><ul><ul><ul><li>Which modality is or should be default? </li></ul></ul></ul><ul><ul><ul><li>Adapting model of attention to situation </li></ul></ul></ul>
  • 18. <ul><ul><li>SwiftFile </li></ul></ul><ul><ul><ul><li>Benefits “without demanding anything?” </li></ul></ul></ul><ul><ul><ul><li>Predictor uses meta-information? </li></ul></ul></ul><ul><ul><ul><li>Evaluation data was fair sample? </li></ul></ul></ul><ul><ul><ul><li>Criteria for success were reasonable, right? </li></ul></ul></ul><ul><ul><ul><li>TF-IDF vs. User-defined rules </li></ul></ul></ul>

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