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RADARMay 5, 2005 1
RADAR/Space-Time Assistant:Crisis Allocation of Resources
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RADARMay 5, 2005 2
Space-Time researchers
JaimeCarbonell
EugeneFink
Faculty
Research staff
Peter Jansen
Students
Chris Martens
UlasBardak
ScottFahlman
SteveSmith
Greg Jorstad
Brandon Rothrock
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RADARMay 5, 2005 3
Outline• Purpose and main challenges
• Demo of Space-Time Assistant
• Current and future learning
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RADARMay 5, 2005 4
Purpose
Automated allocation of rooms andrelated resources, in both crisis androutine situations.
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RADARMay 5, 2005 5
Motivating taskScheduling of talks at a conference,and related allocation of rooms andequipment, in a crisis situation.
• Initial schedule• Unexpected major change in
room availability; for example,closing of a building
• Continuous stream of minor changes;for example, schedule changes and unforeseen equipment needs
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RADARMay 5, 2005 6
Main challenges• Effective resource allocation
• Collaboration with thehuman administrator
• Use of uncertain knowledge
• Dealing with surprises
• Information elicitation
• Learning of new strategies
running
currentwork
futurework
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RADARMay 5, 2005 7
Architecture
Info elicitorParser Optimizer
Processnew info
Updateconferenceschedule
Chooseand sendquestions
Top-level controland learning
Graphicaluser interface
Administrator
Future Work
RADAR 1.0
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RADARMay 5, 2005 8
Outline• Purpose and main challenges
• Demo of Space-Time Assistant
• Current and future learning
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RADARMay 5, 2005 9
Outline• Purpose and main challenges
• Demo of Space-Time Assistant
• Current and future learning
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RADARMay 5, 2005 10
• Information elicitation
Learningcurrent work(RADAR 1.0)
• Learning of relevant questions
• Learning of typical requirements and default user preferences
near future(Years 2–3)
Years 3–5• Learning of new strategies
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RADARMay 5, 2005 11
• The system learns most of the new knowledge during “war games”
• It may learn some additional knowledge during the test
Learning
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RADARMay 5, 2005 12
Information elicitationThe system identifies critical missing knowledge, sends related questions to users, and improves the world model based on their answers.
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RADARMay 5, 2005 13
Information elicitationInput:• Uncertain information about resources,
requirements, and user preferences• Answers to the system’s questionsLearned knowledge:• Critical additional information about resources,
requirements, and preferencesKnowledge examples:• Size of the auditorium is 5000 ± 50 square feet• Size of the broom closet does not matterUseful when the initial knowledge includes significant uncertainty, and users are willing to answer the system’s questions.
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RADARMay 5, 2005 14
Learning of relevant questionsThe system analyzes old elicitation logs and creates rules for “static” generation of useful questions, which allow asking critical questions before scheduling.
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RADARMay 5, 2005 15
Learning of relevant questionsInput:• Log of the information elicitationLearned knowledge:• Rules for question generationKnowledge examples:• If the size of the largest room is unknown,
ask about its size before scheduling• Never ask about the sizes of broom closetsUseful when the knowledge includes significant uncertainty, users answer the system’s questions, and “war games” provide sufficient information for learning appropriate rules.
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RADARMay 5, 2005 16
Learning of default preferencesThe system analyzes known requirements and user preferences, creates rules for generating default preferences, and uses them to make assumptions about unknown preferences.
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RADARMay 5, 2005 17
Learning of default preferencesInput:• Known requirements and preferences• Answers to the system’s questionsLearned knowledge:• Rules for generating default
requirements and preferencesKnowledge examples:• Regular session needs a projector
with 99% certainty• When John Smith gives keynote talks,
he always uses a microphoneUseful when “war games” provide sufficient information for learning appropriate defaults.
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RADARMay 5, 2005 18
• The system’s knowledge during “war games” includes significant uncertainty
• Users can obtain additional information in response to the system’s questions
• The world model and schedule properties during “war games” are similar to those during follow-up tests
Effective “war games”