1. RADAR/Space-Time: Allocation of Rooms and Vendor Orders
June 29, 2006

2. People Faculty Research staff Grad students Part-time staff
Matt Jennings
to be hired...
Jaime Carbonell
Steve Smith
Eugene Fink
Grad students
Kostya Salomatin
Ulas Bardak
Part-time staff
Steve Gardiner
Vijay Prakash
Colin Jarvis
Blaze Iliev
June 29, 2006

3. Problem Scheduling of talks at a conference,
and related allocation of rooms and
vendor orders, in a crisis situation.
Initial schedule
Major change in space availability
Continuous stream of minor changes
June 29, 2006

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
June 29, 2006

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.
June 29, 2006

6. Architecture Top-level control Graphical user interface Info elicitor
Parser
Optimizer
Process new info
Update resource allocation
Choose and send questions
June 29, 2006

7. Place in RADAR ST GUI ST Module AnnoDB User-Initiated SCONE Vendors
CLASSIFIER TA
provides data about resources
TASKS
SCHEDULE
VIO
helps to obtain additional rooms
CMRadar
ST GUI
ST Module
SpaceTime
publishes
the schedule
WbE
June 29, 2006

8. Optimization experiments
Schedule Quality
Manual and auto scheduling
problem size 10
Search time
0.8
0.9
0.7
0.6 1 2 3 4 5 6 7 8 9
Schedule Quality
Time (seconds)
13 rooms
84 events
Manual
Auto
0.78
5 rooms
32 events
0.80
Manual
Auto
0.83
0.72
9 rooms
62 events
Manual
Auto
0.83
0.63
13 rooms
84 events
without
uncertainty
with
uncertainty
June 29, 2006

9. Elicitation experiments
We have applied the system to repair a schedule after a crisis loss of rooms.
0.68
0.72
Schedule Quality 10 30 20 40 50
Number of Questions
Dependency of the quality on the number of questions
Schedule Quality
Manual and auto repair
Auto with Elicitation
0.72
Auto w/o Elicitation
0.68
Manual Repair
0.61
After Crisis
0.50
June 29, 2006

10. Future challenges (Years 3–4)
Auto vendor orders
Contingency plans
Common-sense rules
Elicitation learning
Tactical research (Year 3)
Auto vendor orders
Contingency plans
Common-sense rules
Elicitation learning
User collaboration
Opportunistic and transfer learning
User collaboration
Opportunistic and transfer learning
Strategic research (Years 3–4 and beyond)
June 29, 2006

11. Main modules Auto vendor orders Contingency plans Common-sense rules
Elicitation learning
Optimizer
Information elicitor
Graphical user
interface
User collaboration
Opportunistic and transfer learning
Top-level control
June 29, 2006

12. Research areas Auto vendor orders Contingency plans Optimization
Common-sense rules
Elicitation learning
Optimization
Learning
User collaboration
Opportunistic and transfer learning
Visualization
June 29, 2006

13. Learning opportunities
Search heuristics
Common-sense rules
Elicitation strategies
Learning
User preferences and collaboration strategies
Unexpected relevant facts and strategies
June 29, 2006

14. Tentative schedule Auto vendor orders Contingency plans July–Oct
Common-sense rules
Elicitation learning
July–Oct
Aug–Jan
User collaboration
Opportunistic and transfer learning
Sept–Y4
June 29, 2006