RADARMay 5, 2005 1

RADAR/Space-Time Assistant:Crisis Allocation of Resources

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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Outline• Purpose and main challenges

• Demo of Space-Time Assistant

• Current and future learning

RADARMay 5, 2005 4

Purpose

Automated allocation of rooms andrelated resources, in both crisis androutine situations.

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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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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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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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Outline• Purpose and main challenges

• Demo of Space-Time Assistant

• Current and future learning

RADARMay 5, 2005 9

Outline• Purpose and main challenges

• Demo of Space-Time Assistant

• Current and future learning

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• 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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• 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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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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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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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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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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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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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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• 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”