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 • Franklin Ho • Blaze Iliev • Vijay Prakash SteveGardiner 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. Architecture Parser Optimizer Info elicitor Updateresourceallocation Chooseand sendquestions Graphicaluser interface Top-level control Processnew info

5. Main results Automated scheduling of a conference, with optional user participation. • Representation of uncertain knowledge • Optimization of room assignments andvendor orders under uncertainty • Elicitation of additional information • Collaboration with the user

6. Main results 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.

7. Recent results • Extended representation - Event properties - Implied properties - Unknown values - Extended elicitation

8. Recent results • Extended representation • Common-sense rules - Representation - Application - Learning

9. Recent results • Extended representation • Common-sense rules • New data model - Application program interface - Integration with database

10. Recent results • Extended representation • Common-sense rules • New data model • Fast optimization

11. Recent results • Extended representation • Common-sense rules • New data model • Fast optimization • Batch learning - Slow any-time learning - Analysis of common-sense rules, uncertainty, and vendor orders

12. Recent results • Extended representation • Common-sense rules • New data model • Fast optimization • Batch learning • Vendor orders - Representation - Optimization - Elicitation

13. Recent results Elicitationlearning • Extended representation • Common-sense rules • New data model • Fast optimization • Batch learning • Vendor orders Learning of common sense

14. Recent results not in the Oct. 15 release • Extended representation • Common-sense rules • New data model • Fast optimization • Batch learning • Vendor orders 100% 95% 80% 95% 80% 50% Completion Detailed task list www.cs.cmu.edu/~eugene/Radar/tasks.txt

15. Future challenges • Room costs • Integration withNL processing • Contingency plans Tactical research(Year 3) • User collaboration • Opportunistic andtransfer learning Strategic research(Years 3–4 and beyond) • Room costs • Integration withNL processing • Contingency plans • User collaboration • Opportunistic andtransfer learning

16. Future challenges Optimization Learning Languageunderstanding Visualization • Room costs • Integration withNL processing • Contingency plans • User collaboration • Opportunistic andtransfer learning • Room costs • Integration withNL processing • Contingency plans • User collaboration • Opportunistic andtransfer learning

17. Future challenges • Room costs • Integration withNL processing • Contingency plans • User collaboration • Opportunistic andtransfer learning • Room costs • Integration withNL processing • Contingency plans January Initial version June • User collaboration • Opportunistic andtransfer learning Year 4