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

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

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

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

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

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

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

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

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

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

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

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

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

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