Scheduling with Uncertain Resources Reflective Agent with Distributed Adaptive Reasoning RADAR.
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Transcript of Scheduling with Uncertain Resources Reflective Agent with Distributed Adaptive Reasoning RADAR.
Scheduling withUncertain Resources
Reflective Agent withDistributed Adaptive Reasoning
RADAR
,but also under crisis conditions
• Help not only in routine situations
Purpose
• Automation of office-management tasks, such as scheduling, e-mail handling, and resource allocation
Outline
• Overview of RADAR
• Resource-allocation system
• Optimization and elicitation
• Current and future challenges
Challenges
• Intelligent performance ofoffice-management tasks
• Collaboration with users
• Continuous learning of new knowledge and strategies
Main components
Planning and coordinationof high-level actions.
WebMaster
Helps create andmaintain web sites.
E-MailOrganizer
Helps filter, sort, and prioritize messages.
CalendarManager
Helps keep track of appointmentsand negotiate meeting times amongmultiple users.
BriefingAssistant
Helps compile reports based on multiple data sources.
ResourceAllocation
Outline
• Overview of RADAR
• Resource-allocation system
• Optimization and elicitation
• Current and future challenges
Purpose
Automated allocation of rooms and
related resources, in both routine and
crisis situations.• Assignment of offices• Reservation of conference rooms• Allocation of furniture, computers,
and other office equipment
Year 1: Office allocation
A prototype system for automated
allocation of offices.
• Satisfying work-related needs of individual users and groups
• Maximizing user satisfaction
Year 1: Office allocation
A prototype system for automated
allocation of offices.
• Processing of natural-language requests
• Effective allocation of office resources
• Interface for a human administrator
Years 2–3: Conference planning
Scheduling of talks at a conference,and related allocation of rooms andequipment, in a crisis situation.
• Initial allocation plan
• Unexpected major change inspace availability; for example,closing of a building
• Continuous stream of minor changes;for example, schedule changes and unforeseen equipment needs
Years 2–3: Conference planning
Scheduling of talks at a conference,and related allocation of rooms andequipment, in a crisis situation.• Temporal reasoning
• Uncertainty tolerance
• Information elicitation
• Collaboration with thehuman administrator
Outline
• Overview of RADAR
• Resource-allocation system
• Optimization and elicitation
• Current and future challenges
Architecture
Info elicitorParser Optimizer
Processnew info
Updateresourceallocation
Chooseand sendquestions
Top-level controland learning
Graphicaluser interface
Administrator
Uncertainty
The system allows uncertainty in the
representation of all variables and
functions in optimization problems.• Uncertain nominals• Uncertain integers• Uncertain utility
Uncertain nominalsAn uncertain nominal value is either a complete unknown or a set of possible values and their probabilities.Example:We have ordered vegetarian meals, but there is a chance that we will receive meals of a wrong type.
Meal-type: 0.90 chance: vegetarian 0.05 chance: regular 0.05 chance: vegan
Uncertain integersAn uncertain integer is either a complete unknown or a probability-density function represented by a set of uniform distributions.
Example:An auditorium has about 600 seats.
Room-size: 0.2 chance: [450..549] 0.6 chance: [550..650]
0.2 chance: [651..750]
0.0020.0040.006
200 400 600 800
Proba-bility
Room Size
00
Uncertain utilitiesAn uncertain utility function may be represented in three ways.• Complete unknown • Piecewise-linear function with
uncertain y-coordinates
0.5
1.0
200 400 600 8000.0
0 Room Size
Quality
• Set of possible piecewise-linear functions and their probabilities
0.2 chance
0.8 chance
OptimizationThe optimization algorithm is based on randomized hill-climbing.
• At each step, reschedule one event
• Stop after finding a local maximumor reaching a time limit
• Search for a schedule with the greatest expected quality
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
Information elicitation
The system identifies critical missing
knowledge, sends related questions to
users, and improves the world model
based on users’ answers.
Missing info:• Invited talk: – Projector need• Poster session: – Room size – Projector need
Assumptions:• Invited talk: – Needs a projector• Poster session: – Small room is OK – Needs no projector
Example: Initial scheduleAvailable rooms:
Roomnum.
Area(feet2)
Proj-ector
123
2,0001,0001,000
YesNoYes
Requests:• Invited talk, 9–10am: Needs a large room• Poster session, 9–11am: Needs a room
1 2
3
Initial schedule:
Talk
Posters
Example: Choice of questions
1 2
3
Initial schedule:
Talk
Posters
Candidate questions:• Invited talk: Needs a projector?• Poster session: Needs a larger room? Needs a projector?
Requests:• Invited talk, 9–10am: Needs a large room• Poster session, 9–11am: Needs a room
Useless info: There are no large rooms w/o a projector×Useless info: There are no unoccupied larger rooms×Potentially useful info√
Example: Improved scheduleRequests:• Invited talk, 9–10am: Needs a large room• Poster session, 9–11am: Needs a room
1 2
3
Initial schedule:
Talk
Posters
Info elicitation:System:Does the poster sessionneed a projector?User:A projector may be useful,but not really necessary.
1 2
3
New schedule:
Talk
Posters
Choice of questions• For each candidate question, estimate the
probabilities of possible answers
• For each question, compute its expected impact on the schedule quality, and select questions with large expected impacts
• For each possible answer, compute the respective change of the schedule quality
ExperimentsWe 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
Outline
• Overview of RADAR
• Resource-allocation system
• Optimization and elicitation
• Current and future challenges
Main results
• Optimization based on uncertainknowledge of available resources and scheduling constraints
• Collaboration with the user
• Elicitation of additional information about resources and constraints
Current work
• Learning of typical requirementsand default user preferences
• Learning of elicitation strategies
• Contingency scheduling
Learning of typical requirementsThe system analyzes known requirements
and user preferences, and creates rules for
generating default requirements.
These rules enable the system to make
reasonable assumptions about unknown
requirements and preferences.
Learning of elicitation strategiesThe system analyzes old elicitation logs
and creates rules for “static” generation
of useful questions.
These rules enable the system to ask
critical questions before scheduling.
Contingency schedulingThe system analyzes multiple possible
scenarios and constructs different
schedules for these scenarios.
It thus reduces real-time re-scheduling
required in crisis situations.