1. Preference Elicitation in Scheduling Problems Ulaş Bardak Ph.D. Thesis Proposal Committee Jaime Carbonell, Eugene Fink, Stephen Smith, Sven Koenig (University of Southern California)

2. Outline Introduction Example Preliminary results Plan of work Outline – Introduction – Example – Preliminary Results – Plan of Work – Questions

3. Motivation Improve resource planning by reducing uncertainty of the available knowledge. Outline – Introduction – Example – Preliminary Results – Plan of Work – Questions

4. Hypothesis By asking the questions with the highest potential to reduce uncertainty, we can improve the quality of the resource plan while minimizing the cost of elicitation. Outline – Introduction – Example – Preliminary Results – Plan of Work – Questions

5. Missing info: Invited talk: – Projector need Poster session: – Room size – Projector need Assumptions: Invited talk: – Needs a projector Poster session: – Small room is OK – Needs no projector Initial schedule Available rooms: Room num. CapacityProjector 123123 500 100 80 Yes No Yes Requests: Invited talk, 9–10am: Needs big room Poster session, 9–11am: Needs a room 1 2 3 Initial schedule: Talk Posters Outline – Introduction – Example – Preliminary Results – Plan of Work – Questions

6. Choice of questions 1 Initial schedule: Talk Posters Candidate questions: Invited talk: Needs a projector? Poster session: Needs a larger room? Needs a projector? Requests: Invited talk, 9–10am: Needs a large room Poster session, 9–11am: Needs a room Useless info: There are no large rooms w/o a projector × Useless info: There are no unoccupied larger rooms × Potentially useful info √ 2 3 Outline – Introduction – Example – Preliminary Results – Plan of Work – Questions

7. Improved schedule Requests: Invited talk, 9–10am: Needs a large room Poster session, 9–11am: Needs a room Info elicitation: System: Does the poster session need a projector? User: A projector may be useful, but not really necessary. Posters 1 Initial schedule: Talk Posters 2 3 1 New schedule: Talk 2 3 Outline – Introduction – Example – Preliminary Results – Plan of Work – Questions

8. Architecture ElicitorNatural Lang.Optimizer Ask user and get answers Choose and send questions Update resource allocation Outline – Introduction – Example – Preliminary Results – Plan of Work – Questions

9. Inside the Elicitor Outline – Introduction – Example – Preliminary Results – Plan of Work – Questions Get list of questions For each question i get utilities for possible answers Return top N questions Get question score Each uncertain variable is a potential question Plug in possible answers to the utility function to get change in utility.

10. Optimizer Uses hill climbing to allocate resources Searches for an assignment of resources with the greatest expected utility Outline – Introduction – Example – Preliminary Results – Plan of Work – Questions

11. Related Work Example critiquing [Burke et al.]  Have users tweak result set Collaborative filtering [Resnick], [Hill et al.]  Have the user rank related items Similarity-based heuristics [Burke]  Look at past similar user ratings Focusing on targeted use [Stolze] Outline – Introduction – Example – Preliminary Results – Plan of Work – Questions

12. Related Work Clustering utility functions [Chajewska] Decision tree [Stolze and Ströbel] Min-max regret [Boutilier]  Choose question that reduces max regret Auctions [Smith], [Boutilier], [Sandholm] Outline – Introduction – Example – Preliminary Results – Plan of Work – Questions

13. What is different? No bootstrapping Continuous variables Large number of uncertain variables Tight integration with the optimizer Integration of multiple approaches Dynamic elicitation costs Outline – Introduction – Example – Preliminary Results – Plan of Work – Questions

14. Example Domain Assigning rooms to conference sessions Rooms have properties. Sessions have preferences, constraints, and importance values. Each preference is a function from a room property to utility. Outline – Introduction – Example – Preliminary Results – Plan of Work – Questions

15. Rooms have properties. Sessions have preferences, constraints, and importance values. Each preference is a function from a room property to utility. Example Domain Outline – Introduction – Example – Preliminary Results – Plan of Work – Questions Room 1 can accommodate 200 people. Room 3 has one projector: 80% chance Room 3 has no projectors : 20% chance Room 3 has one projector: 80% chance Room 3 has no projectors : 20% chance

16. Rooms have properties. Sessions have preferences, constraints, and importance values. Each preference is a function from a room property to utility. Example Domain Outline – Introduction – Example – Preliminary Results – Plan of Work – Questions Room 3 has one projector: 80% chance Room 3 has no projectors : 20% chance Room 3 has one projector: 80% chance Room 3 has no projectors : 20% chance Invited talk cannot be before 2 p.m. Invited talk is more important than poster session. Invited talk cannot be before 2 p.m. Invited talk is more important than poster session. Invited talk very important: 40% chance Invited talk moderately important: 60% chance Invited talk very important: 40% chance Invited talk moderately important: 60% chance

17. Rooms have properties. Sessions have preferences, constraints, and importance values. Each preference is a function from a room property to utility. Example Domain Outline – Introduction – Example – Preliminary Results – Plan of Work – Questions Capacity of Room 1 is 200. Capacity preference: 150 people is minimum, 200 people is acceptable, 250 people is best. Capacity preference: 150 people is minimum, 200 people is acceptable, 250 people is best. Invited talk very important: 40% chance Invited talk moderately important: 60% chance Invited talk very important: 40% chance Invited talk moderately important: 60% chance Capacity preference is [150, 200, 250]: 40% chance Capacity preference is [50, 100, 150]: 60% chance Capacity preference is [150, 200, 250]: 40% chance Capacity preference is [50, 100, 150]: 60% chance Room 3 has one projector: 80% chance Room 3 has no projectors : 20% chance Room 3 has one projector: 80% chance Room 3 has no projectors : 20% chance

18. Experiments Evaluation of RADAR 15 room properties 88 rooms 84 sessions 2500 variables 700 uncertain values System asked to provide 50 top questions. Outline – Introduction – Example – Preliminary Results – Plan of Work – Questions

19. 0.72 0.78 Utility 1030204050 No. of Questions 0.58 Certain Incremental Optimizer estimate Outline – Introduction – Example – Preliminary Results – Plan of Work – Questions Incremental elicitation

20. Completed work Questions based on potential reduction of uncertainty Empirical evaluation Integration with RADAR Outline – Introduction – Example – Preliminary Results – Plan of Work – Questions

21. Contributions Fast computation of expected impact for potential questions Use of the optimizer for calculating more accurate question weights. Use of past elicitation results to improve the elicitation process. Unifying different elicitation strategies. √ Outline – Introduction – Example – Preliminary Results – Plan of Work – Questions

22. Search for optimal questions Outline – Introduction – Example – Preliminary Results – Plan of Work – Questions 100-150:40% chance 151-200:60% chance 160-200:50% chance100-160:50% chance 100-130:25% chance130-160:25% chance h=20, max utility increase = 20 h=10, max utility increase = 30h=10 h=15, max utility increase = 100 h=15 Best-first search with the optimizer used as the heuristic function. Example: Uncertain room size

23. Elicitation rules Encoding of elicitation heuristics Outline – Introduction – Example – Preliminary Results – Plan of Work – Questions rule Uncertain-Auditorium-Size(room)

24. Learning of elicitation rules Derive rules based on past elicitations Outline – Introduction – Example – Preliminary Results – Plan of Work – Questions rule Learned-Rule(room,event) …Event Imp. Room Prop. Mean Prop. Elicit. Result …110Proj.0.5+ …115Size250- …105Proj.0.9+ …90Proj.0.5- …200Size100+ …150Proj.0.3+ …Session Type Room Type Event Importance Room Property Mean Value Elicitation Result …Invited TalkAuditorium110Proj.0.5+ …PostersMeeting R.115Size250- …Best PaperAuditorium105Proj.0.9+ …PostersClassroom90Proj.0.5- …TalkAuditorium200Size100+ …KeynoteAuditorium150Proj.0.3+

25. Dynamic question costs Same cost for all questions Different cost for different question types Learning of the question costs for each type Learning of the question costs for each information source Outline – Introduction – Example – Preliminary Results – Plan of Work – Questions √ √

26. Compare different approaches: Current system Search for optimal questions Hand coded elicitation rules Learned elicitation rules Unified system Human elicitor Measure utility gain after each answer; also evaluate running time Experiments Outline – Introduction – Example – Preliminary Results – Plan of Work – Questions

27. Timeline Best-First Search Syntax for rules Learning of rules Experiments Aug 2007 Mar 2007 Nov 2006 July 2006 Mar 2006 Unified System Dec 2007 Outline – Introduction – Example – Preliminary Results – Plan of Work – Questions Writing Learning of costs

29. Addendum Outline – Introduction – Example – Preliminary Results – Plan of Work – Questions