1. Entropy-Driven Online Active Learning for Interactive Calendar Management Julie S. Weber Martha E. Pollack

2. 2 Personal Assistants Electronic meeting requests via email Calendar Assistants |PTIME|Active Learning|Algorithm|Results|Conclusions 562 messages this week

3. 3 Personal Assistants Key Requirement: Knowledge of Scheduling Preferences Calendar Assistants |PTIME|Active Learning|Algorithm|Results|Conclusions Challenge: Preference Elicitation in an Interactive Environment

4. 4 Challenges of an Interactive Environment Must be opportunistic Calendar Assistants |PTIME|Active Learning|Algorithm|Results|Conclusions Must balance efficient learning and user satisfaction

5. 5 Example Meeting Request Meet Monday afternoon or Friday lunch? Monday @ 1pm Monday @ 1:30 Monday @ 2pm Monday @ 2:30 Monday @ 3pm Friday @ 12pm Friday @ 12:30 Friday @ 1pm Meeting Request Meet Monday afternoon or Friday lunch? Solution Set Monday @ 1:30 Monday @ 2:30 Friday @ 12:30 Presentation Set

6. 6 Outline EDALS: Entropy-Driven Active Learning for Scheduling Experimental Analysis Conclusions & Future Work Calendar Management Systems PTIME Active Learning Calendar Assistants|PTIME|Active Learning|Algorithm|Results|Conclusions

7. 7 Calendar Management Systems Kozierok & Maes Reinforcement Learning Mitchell, et al. – CAP Berry, et al. – PTIME General calendar management Calendar Assistants |PTIME|Active Learning|Algorithm|Results|Conclusions

8. 8 Personal Time Manager – PTIME Part of CALO: General Personal Assistant Interactive Calendar Management Key Learning Component: PLIANT Preference Learner Active Learner Berry, et al. 2005 Calendar Assistants| PTIME |Active Learning|Algorithm|Results|Conclusions

9. Calendar Manager Constraint Reasoner Active Learner preference profile Preference Learner PLIANT ranked presentation set scheduling request candidates selected candidate 3 solution set 7 4 5 2 1 6 Ranker Calendar Assistants| PTIME |Active Learning|Algorithm|Results|Conclusions

10. 10 Schedule Features Local FeaturesFeature Values Day of weekMon, tue, wed, thu, fri Start timeEarly/late am, lunch, early/late pm DurationShort, med-short, med-long, long TypeColleague, dean, student, talk, other Global Features (free time) Short free blocksNone, few, some, many Medium free blocksNone, few, some, many Long free blocksNone, few, some, many Global Features (overlaps) Colleague/{Coll.,Dean,Stud.,Talk,Other}None, few, some, many Dean/{Dean,Student,Talk,Other}None, few, some, many Student/{Student,Talk,Other}None, few, some, many Talk/{Talk,Other}None, few, some, many Other/OtherNone, few, some, many

11. Calendar Manager Constraint Reasoner Active Learner preference profile Preference Learner PLIANT ranked presentation set scheduling request candidates selected candidate 3 solution set 7 4 5 2 1 6 Ranker Calendar Assistants| PTIME |Active Learning|Algorithm|Results|Conclusions

12. 12 Active Learning Yu (2005) – Selective Sampling for Ranking Selection driven by ambiguity metric Gervasio, et al. (2005) – A.L. in PTIME Comparison of static active learning techniques Calendar Assistants|PTIME| Active Learning |Algorithm|Results|Conclusions

13. 13 Comparison of Active Learning Techniques Directed techniques Max Diversity Max Novelty Undirected techniques Greedy ε – Greedy Random Calendar Assistants|PTIME| Active Learning |Algorithm|Results|Conclusions Gervasio, et al. ( 2005 ) (+ Best)

14. 14 New Selection Strategy Undirected > Directed ( slightly ) Evaluation criteria Hypotheses: Selection strategy influenced by characteristics of solution set Combined technique may be effective Diversity of solution set an interesting metric Learning efficiency + user satisfaction Calendar Assistants|PTIME| Active Learning |Algorithm|Results|Conclusions

15. 15 EDALS: Entropy-Driven Active Learning for Scheduling Online algorithm that selects between High-diversity solution set => coarse- grained learning => Max Diversity Low-diversity solution set => fine- grained learning => ε – Greedy Based on the entropy of the solution set Calendar Assistants|PTIME|Active Learning| Algorithm |Results|Conclusions

16. 16 Solution Set Entropy Calendar Assistants|PTIME|Active Learning| Algorithm |Results|Conclusions Entropy of a single feature: DayTimeDurTypeSFreeMFreeLFree O12O12 O13O13· · · 100000010010001000000100100001010000100· · · 001001000010001000000100100001010000100· · · 001000001010001000000100100001010000100· · · 001000000101001000000100100001010000100· · ·

17. 17 Solution Set Entropy Calendar Assistants|PTIME|Active Learning| Algorithm |Results|Conclusions Entropy of a single feature: Total average entropy:

18. 18 EDALS Choose_Method( S ) 1. E calculate_entropy( S ) 2. If E ≤ threshold 3. return Undirected( S ) 4. Else 5. return Directed( S ) Calendar Assistants|PTIME|Active Learning| Algorithm |Results|Conclusions

19. 19 Threshold Determination Calendar Assistants|PTIME|Active Learning|Algorithm| Results |Conclusions ClassBest Performance  Tight,3  High Threshold (0.9)  Loose,3 ,  Loose,6  Mid-range Threshold (0.6, 0.7)  Tight,6  Low Threshold (0.3)

20. 20 Experiments 1. Determine best threshold value for EDALS 2. Determine best EDALS components 3. Compare EDALS against static selection strategies Calendar Assistants|PTIME|Active Learning|Algorithm| Results |Conclusions

21. 21 Experimental Setup User commitment level: high & low (18 vs. 3 meetings) Number of disjunctions in request (<=3 vs. <=6) Tightness of constraints (loose vs. tight) 20 simulated users of each type (user evaluation functions) 1000 meeting requests per simulated user Presentation set size = 5 Calendar Assistants|PTIME|Active Learning|Algorithm| Results |Conclusions

22. 22 Performance Criteria Spearman’s Correlation Coefficient Difference between system’s ranking and user’s ranking of 100 schedules User Satisfaction Rank of the best option in presentation set compared to other feasible alternatives Calendar Assistants|PTIME|Active Learning|Algorithm| Results |Conclusions

23. 23 Initial EDALS Experiment Calendar Assistants|PTIME|Active Learning|Algorithm| Results |Conclusions  Loose,3  - Spearman Correlation

24. 24 Static Selection Techniques Calendar Assistants|PTIME|Active Learning|Algorithm| Results |Conclusions Seeded - SpearmanUnseeded - Spearman Unseeded – User SatisfactionSeeded – User Satisfaction

25. 25 EDALS Component Selection Calendar Assistants|PTIME|Active Learning|Algorithm| Results |Conclusions Spearman User Satisfaction

26. 26 EDALS vs. Static Techniques Calendar Assistants|PTIME|Active Learning|Algorithm| Results |Conclusions Seeded - SpearmanUnseeded - Spearman Unseeded – User SatisfactionSeeded – User Satisfaction

27. 27 Conclusion An online approach to active learning appears both learning- effective and user-satisfying in the scheduling preference-learning domain Calendar Assistants|PTIME|Active Learning|Algorithm|Results| Conclusions

28. 28 Future Work Human users Calendar Assistants|PTIME|Active Learning|Algorithm|Results| Conclusions Dynamic thresholds Adjustable autonomy Application to other domains