RADAR /Space-Time: Allocation of Rooms and Vendor Orders

1. RADAR/Space-Time:Allocation of Roomsand Vendor Orders

2. People Research staff MattJennings to behired... Grad students Part-time staff • Steve Gardiner • Vijay Prakash • Colin Jarvis • Blaze Iliev UlasBardak KostyaSalomatin Faculty JaimeCarbonell SteveSmith EugeneFink

3. Problem • Initial schedule • Major change inspace availability • Continuous streamof minor changes Scheduling of talks at a conference, and related allocation of rooms and vendor orders, in a crisis situation.

4. Current results (Year 2) Automated scheduling of a conference, with optional user participation. • Representation of uncertain knowledge • Optimization under uncertainty • Elicitation of additional information • Collaboration with the user

5. Current results (Year 2) Four conference papers: • Bardak, Fink, and Carbonell. Scheduling with uncertain resources: Representation and utility function. IEEE SMC Conference, 2006. • Fink, Jennings, Bardak, Oh, Smith, and Carbonell. Scheduling with uncertain resources: Search for a near-optimal solution. IEEE SMC Conference, 2006. • Bardak, Fink, Martens, and Carbonell. Scheduling with uncertain resources: Elicitation of additional data. IEEE SMC Conference, 2006. • Fink, Bardak, Rothrock, and Carbonell. Scheduling with uncertain resources: Collaboration with the user. IEEE SMC Conference, 2006.

6. Architecture Parser Optimizer Info elicitor Updateresourceallocation Chooseand sendquestions Graphicaluser interface Top-level control Processnew info

7. Place in RADAR AnnoDB SCONE CLASSIFIER TA provides dataabout resources helps to obtainadditional rooms ST GUI publishes the schedule ST Module User-Initiated Vendors TASKS EMAIL SCHEDULE VIO CMRadar SpaceTime WbE

8. Optimization experiments Manual and auto scheduling Search time ScheduleQuality ScheduleQuality 0.83 0.83 0.80 0.78 0.72 Auto Auto Auto 0.63 Manual 0.9 Manual Manual 0.8 0.7 0.6 4 1 3 9 2 5 6 7 8 10 13 rooms 84 events 5 rooms 32 events 9 rooms 62 events Time (seconds) 13 rooms 84 events problem size without uncertainty with uncertainty

9. Elicitation experiments Dependency of the qualityon the number of questions Manual and auto repair ScheduleQuality ScheduleQuality 0.72 0.68 0.72 0.61 Auto withElicitation 0.50 Auto w/oElicitation ManualRepair After Crisis 0.68 10 30 40 50 20 Number of Questions We have applied the system to repair a schedule after a crisis loss of rooms.

10. Future challenges (Years 3–4) • Auto vendor orders • Contingency plans • Common-sense rules • Elicitation learning Tactical research(Year 3) • User collaboration • Opportunistic andtransfer learning Strategic research(Years 3–4 and beyond) • Auto vendor orders • Contingency plans • Common-sense rules • Elicitation learning • User collaboration • Opportunistic andtransfer learning

11. Main modules Optimizer Informationelicitor Graphical user interface Top-level control • Auto vendor orders • Contingency plans • Common-sense rules • Elicitation learning • User collaboration • Opportunistic andtransfer learning

12. Research areas Optimization Learning Visualization • Auto vendor orders • Contingency plans • Common-sense rules • Elicitation learning • User collaboration • Opportunistic andtransfer learning

13. Learning opportunities • Search heuristics • Common-sense rules • Elicitation strategies Learning • User preferences and collaboration strategies • Unexpected relevantfacts and strategies

14. Tentative schedule July–Oct Aug–Jan Sept–Y4 • Auto vendor orders • Contingency plans • Common-sense rules • Elicitation learning • User collaboration • Opportunistic andtransfer learning