radar /space-time: allocation of rooms and vendor orders
DESCRIPTION
RADAR /Space-Time: Allocation of Rooms and Vendor Orders. People. Research staff. Matt Jennings. to be hired. Grad students. Part-time staff. Steve Gardiner Vijay Prakash Colin Jarvis Blaze Iliev. Ulas Bardak. Kostya Salomatin. Faculty. Jaime Carbonell. Steve Smith. Eugene Fink. - PowerPoint PPT PresentationTRANSCRIPT
RADAR June 29, 2006 1
RADAR/Space-Time:Allocation of Roomsand Vendor Orders
RADAR June 29, 2006 2
People
JaimeCarbonell
EugeneFink
Faculty
SteveSmith
Part-time staff• Steve Gardiner• Vijay Prakash• Colin Jarvis• Blaze Iliev
Grad students
KostyaSalomatin
UlasBardak
Research staff
MattJennings
to behired...
RADAR June 29, 2006 3
ProblemScheduling of talks at a conference,and related allocation of rooms andvendor orders, in a crisis situation.
• Initial schedule
• Major change inspace availability
• Continuous streamof minor changes
RADAR 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
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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.
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Architecture
Info elicitorParser Optimizer
Processnew info
Updateresourceallocation
Chooseand sendquestions
Top-level control
Graphicaluser interface
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Place in RADAR
AnnoDB
SCONE
TASKS
CLASSIFIER
TA
User-Initiated
CMRadar
SpaceTime
SCHEDULE VIO
WbE
Vendors
ST GUI
ST Module
provides dataabout resources
helps to obtainadditional rooms
publishesthe schedule
RADAR June 29, 2006 8
Optimization experiments
Manual
Auto
0.830.72
9 rooms62 events
Manual
Auto
0.83
0.63
13 rooms84 events
withoutuncertainty
withuncertainty
10
Search time
0.8
0.9
0.7
0.61 2 3 4 5 6 7 8 9
ScheduleQuality
Time (seconds)13 rooms84 events
Manual
Auto
0.78
5 rooms32 events
0.80
ScheduleQuality
Manual and auto scheduling
problem size
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Elicitation experiments
We have applied the system to repair a schedule after a crisis loss of rooms.
After
Crisis
0.50 Manual
Repair
0.61 Auto w
/oE
licitation
0.68 Auto w
ithE
licitation
0.72
ScheduleQuality
Manual and auto repair
0.68
0.72
ScheduleQuality
10 3020 40 50Number of Questions
Dependency of the qualityon the number of questions
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• Auto vendor orders• Contingency plans• Common-sense rules• Elicitation learning• User collaboration• Opportunistic and
transfer learning
Future challenges (Years 3–4)
• Auto vendor orders• Contingency plans• Common-sense rules• Elicitation learning
Tactical research(Year 3)
• User collaboration• Opportunistic and
transfer learning
Strategic research(Years 3–4 and beyond)
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Main modules
• Auto vendor orders• Contingency plans• Common-sense rules• Elicitation learning• User collaboration• Opportunistic and
transfer learning
Optimizer
Graphical userinterfaceTop-level
control
Informationelicitor
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Research areas
• Auto vendor orders• Contingency plans• Common-sense rules• Elicitation learning• User collaboration• Opportunistic and
transfer learning
Optimization
Learning
Visualization
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Learning opportunities
• Search heuristics• Common-sense rules• Elicitation strategies• User preferences and
collaboration strategies• Unexpected relevant
facts and strategies
Learning
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Tentative schedule
• Auto vendor orders• Contingency plans• Common-sense rules• Elicitation learning• User collaboration• Opportunistic and
transfer learning
July–Oct
Aug–Jan
Sept–Y4