entropy-driven online active learning for interactive calendar management julie s. weber martha e....

Entropy-Driven Online Active Learning for Interactive Calendar Management

Julie S WeberMartha E Pollack

2

Personal Assistants

bull Electronic meeting requests via email

Calendar Assistants|PTIME|Active Learning|Algorithm|Results|Conclusions

562 messages this week

3

Personal Assistants

bull Key Requirement Knowledge of Scheduling Preferences

Calendar Assistants|PTIME|Active Learning|Algorithm|Results|Conclusions

bull Challenge Preference Elicitationin an Interactive Environment

4

Challenges of an Interactive Environment

bull Must be opportunistic

Calendar Assistants|PTIME|Active Learning|Algorithm|Results|Conclusions

bull Must balance efficient learning and user satisfaction

5

Example

Meeting RequestMeet Monday afternoon or Friday lunch

Monday 1pm

Monday 130

Monday 2pm

Monday 230

Monday 3pm

Friday 12pm

Friday 1230

Friday 1pm

Meeting Request

Meet Monday afternoon or Friday lunch

SolutionSet

Monday 130

Monday 230

Friday 1230

Presentation Set

6

Outline

bull EDALS Entropy-Driven Active Learning for

Schedulingbull Experimental Analysis

bull Conclusions amp Future Work

bull Calendar Management Systemsbull PTIME

bull Active Learning

Calendar Assistants|PTIME|Active Learning|Algorithm|Results|Conclusions

7

Calendar Management Systems

bull Kozierok amp Maesbull Reinforcement Learning

bull Mitchell et al ndash CAP bull Berry et al ndash PTIME

bull General calendar management

Calendar Assistants|PTIME|Active Learning|Algorithm|Results|Conclusions

8Personal Time Manager ndash PTIME

bull Part of CALO General Personal Assistant

bull Interactive Calendar Managementbull Key Learning Component PLIANT

bull Preference Learnerbull Active Learner

Berry et al 2005

Calendar Assistants|PTIME|Active Learning|Algorithm|Results|Conclusions

CalendarManager

ConstraintReasoner

ActiveLearner

preferenceprofile

PreferenceLearner

PLIANT

ranked

presentation set

scheduling request

candidates

selectedcandidate

3

solutionset

7

4

5

2

1

6

Ranker

Calendar Assistants|PTIME|Active Learning|Algorithm|Results|Conclusions

10

Schedule FeaturesLocal Features Feature ValuesDay of week Mon tue wed thu fri

Start time Earlylate am lunch earlylate pm

Duration Short med-short med-long long

Type Colleague dean student talk other

Global Features (free time)Short free blocks None few some many

Medium free blocks None few some many

Long free blocks None few some many

Global Features (overlaps)ColleagueCollDeanStudTalkOther

None few some many

DeanDeanStudentTalkOther None few some many

StudentStudentTalkOther None few some many

TalkTalkOther None few some many

OtherOther None few some many

CalendarManager

ConstraintReasoner

ActiveLearner

preferenceprofile

PreferenceLearner

PLIANT

ranked

presentation set

scheduling request

candidates

selectedcandidate

3

solutionset

7

4

5

2

1

6

Ranker

Calendar Assistants|PTIME|Active Learning|Algorithm|Results|Conclusions

12

Active Learning

bull Yu (2005) ndash Selective Sampling for Rankingbull Selection driven by ambiguity metric

bull Gervasio et al (2005) ndash AL in PTIMEbull Comparison of static active learning

techniques

Calendar Assistants|PTIME|Active Learning|Algorithm|Results|Conclusions

13

Comparison of Active Learning Techniques

bull Directed techniquesbull Max Diversitybull Max Novelty

bull Undirected techniquesbull Greedybull ε ndash Greedybull Random

Calendar Assistants|PTIME|Active Learning|Algorithm|Results|Conclusions

Gervasio et al (2005)

(+ Best)(+ Best)

(+ Best)

14

New Selection Strategy

bull Undirected gt Directed (slightly)bull Evaluation criteria

bull Hypothesesbull Selection strategy influenced by

characteristics of solution setbull Combined technique may be effectivebull Diversity of solution set an interesting

metricbull Learning efficiency + user satisfaction

Calendar Assistants|PTIME|Active Learning|Algorithm|Results|Conclusions

15EDALS Entropy-Driven Active Learning for Scheduling

bull Online algorithm that selects betweenbull High-diversity solution set =gt coarse-

grained learning =gt Max Diversity bull Low-diversity solution set =gt fine-

grained learning =gt ε ndash Greedy

Based on the entropy of the solution set

Calendar Assistants|PTIME|Active Learning|Algorithm|Results|Conclusions

16

Solution Set Entropy

Calendar Assistants|PTIME|Active Learning|Algorithm|Results|Conclusions

)1(log)1(||

1||

ij

f

jfijf fPfPE

i

ii

Entropy of a single feature

Day Time Dur Type SFree

MFree

LFree

O12 O13

10000

00100

1000

10000

0010 0100 0010 1000

0100

00100

10000

1000

10000

0010 0100 0010 1000

0100

00100

00010

1000

10000

0010 0100 0010 1000

0100

00100

00001

0100

10000

0010 0100 0010 1000

0100

17

Solution Set Entropy

Calendar Assistants|PTIME|Active Learning|Algorithm|Results|Conclusions

)1(log)1(||

1||

ij

f

jfijf fPfPE

i

ii

Entropy of a single feature

Ff

Fff

i

f

Ef

E i

i

||

||1

Total average entropy

18

EDALS

Choose_Method(S)

1 E calculate_entropy(S)

2 If E le threshold

3 return Undirected(S)

4 Else

5 return Directed(S)

Calendar Assistants|PTIME|Active Learning|Algorithm|Results|Conclusions

20

Experiments

1 Determine best threshold value for EDALS2 Determine best EDALS components 3 Compare EDALS against static selection strategies

Calendar Assistants|PTIME|Active Learning|Algorithm|Results|Conclusions

22

Performance Criteria

bull Spearmanrsquos Correlation Coefficientbull Difference between systemrsquos ranking

and userrsquos ranking of 100 schedules

bull User Satisfactionbull Rank of the best option in

presentation set compared to other feasible alternatives

Calendar Assistants|PTIME|Active Learning|Algorithm|Results|Conclusions

23

Initial EDALS Experiment

Calendar Assistants|PTIME|Active Learning|Algorithm|Results|Conclusions

Loose3 - Spearman Correlation

24Static Selection Techniques

Calendar Assistants|PTIME|Active Learning|Algorithm|Results|Conclusions

Seeded - SpearmanUnseeded - Spearman

Unseeded ndash User Satisfaction

Seeded ndash User Satisfaction

25EDALS Component Selection

Calendar Assistants|PTIME|Active Learning|Algorithm|Results|Conclusions

Spearman User Satisfaction

26EDALS vs Static Techniques

Calendar Assistants|PTIME|Active Learning|Algorithm|Results|Conclusions

Seeded - SpearmanUnseeded - Spearman

Unseeded ndash User Satisfaction

Seeded ndash User Satisfaction

27

Conclusion

bull 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

Future Work

bull Human users

Calendar Assistants|PTIME|Active Learning|Algorithm|Results|Conclusions

bull Dynamic thresholds

bull Adjustable autonomybull Application to other domains