RADAR Scheduling Task

1
RADAR Scheduling Task
May 20, 2003

• Manuela Veloso, Stephen Smith,
• Jaime Carbonell, Brett Browning,
• (Jay Modi, Eugene Fink)

2
The Challenge

• Main Functions -- Calendar Management
• Respond to meeting requests (extracted from
  ongoing email stream)
• Initiate meetings requests and establish meetings
• Continuously acquire user preferences and
  negotiation profiles
• Why not yet available
• Requires capture and use of complex,
  ill-structured user preferences
• Continuous scheduling
• Management of rich multi-threaded information
  exchange under conflicting constraints and
  preferences
• Why now
• Explore collaborative, user EPCA, scheduling
• Build upon integration of many leading
  technologies, I.e., information extraction,
  constraint satisfaction, iterative scheduling
• Log, analyze, learn profiles to incrementally
  improve scheduling

3
Calendar Scheduling is Complicated

• Meeting constraints may be hard to satisfy,
  requiring counter proposals, or relaxing, or
  negotiation
• Pre-emption of a meeting can cause a ripple
  effect
• Users do not put all commitments in their
  calendars
• It may be necessary to secure additional
  resources (e.g., room, projection facilities)
• Preferences and interaction protocols will vary
  according to context and participants involved
• There may be several meeting requests in various
  stages of commitment at any given time

4
Diversity and Complexity

• Can we meet tomorrow at 10am?
• Can we meet with Pat some time this week?
• The admissions committee needs to meet every week
  until the end of February.
• The interested teaching AI faculty need to meet
  to schedule the courses for the Fall.
• We should arrange an AI retreat, as the one we
  did a few years ago.

Templates
5
The Approach
User
Editor
Calendar Display
Preferences and Profiles
Learning Processes
Email Stream
Knowledge Base
Extractor
Message Stream
Scheduler
Manager
Email Stream
Need for Sliding Autonomy
6
Scheduler Responding to a Request

• Request Template, T
• When Thursday 15th
• Duration 1 hour
• Who Visiting Researcher (Priority medium)
• Where 1502E NSH
• Response, R
• 400 - 600

but would 1/2 hour be sufficient?
Policy preference Avoid lunch hour
Pending reservation but lower priority

Preference Order 400 - 600 200 - 300 1100-
1200
Evaluate Options
Threshold
Generate Options
7
Manager Multi-Thread Processing
Steve
Student
Raj
10am?
Manuela
Time
Confirmed
Pending
Student, Steve
10am
12pm
Meeting request for blocked time
Manuelas Calendar
2pm
4pm
8
Manager Multi-Thread Processing
Steve
Student
Resch. 12pm?
Raj
10am?
Manuela
Time
Confirmed
Pending
Student, Steve
Raj
10am
Student, Steve
12pm
Conflict try rescheduling
Manuelas Calendar
2pm
4pm
9
Manager Multi-Thread Processing
Steve
Student
Resch. 12pm?
Raj
10am?
Manuela
Time
Confirmed
Pending
Student, Manuela
10am
Student
Student, Manuela
12pm
Conflict try rescheduling
Steves Calendar
2pm
4pm
10
Manager Multi-Thread Processing
Steve
Student
Resch. 12pm?
Raj
10am?
Manuela
Time
Confirmed
Pending
Manuela, Steve
10am
Manuela, Steve
12pm
No conflict
Students Calendar
2pm
4pm
11
Manager Multi-Thread Processing
Steve
Student
Resch. 12pm?
Raj
10am?
Manuela
Time
Confirmed
Pending
Student, Steve
Raj
10am
Student, Steve
12pm
Another meeting
Manuelas Calendar
2pm
Brett
4pm
12
Manager Multi-Thread Processing
Steve
Student
Resch. 12pm?
Raj
10am?
Manuela
Time
Confirmed
Pending
Student, Manuela
10am
Student
12pm
Rescheduling difficult suggest an alternative
Steves Calendar
Student, Manuela
2pm
Student, Manuela
4pm
13
Manager Multi-Thread Processing
Steve
Student
Resch. 12pm?
Raj
10am?
Manuela
Time
Confirmed
Pending
Student, Steve
Raj
10am
12pm
Choose best alternative
Manuelas Calendar
Student, Steve
2pm
Brett
4pm
14
Manager Multi-Thread Processing
Steve
Student
Resch. 12pm?
Raj
10am?
Manuela
Time
Confirmed
Pending
Manuela, Steve
10am
12pm
Pending
Students Calendar
Manuela, Steve
2pm
4pm
15
Manager Multi-Thread Processing
Steve
Student
Resch. 12pm?
Raj
10am?
Manuela
Time
Confirmed
Pending
Raj
10am
12pm
Confirmed
Manuelas Calendar
Student, Steve
2pm
Brett
4pm
16
Main Tasks

• RESPONDING to a request for availability
• Multi-thread conflicting
• request, availability, response, reschedule
• INITIATING organizing a meeting
• Request meeting
• Collect replies
• Merge and solve scheduling
• Until solution is found
• LEARNING
• Priorities, contexts, profiles

17
Manager and Scheduler
Knowledge Base

• Preferences
• Profiles

Email Extractor
T
Manager
Scheduler
Email Generator
18
Manager and Scheduler
Knowledge Base
Email Extractor
T
Manager
Scheduler
T
Email Generator
19
The Science

• Algorithms
• Dynamic, incremental constraint-based reasoning
• Priority-, preference-driven minimum disruption
  optimization
• Main open questions
• How to effectively computer assist a user in
  calendar management?
• How to represent and exploit an ill-structured
  set of calendar scheduling preferences and
  profiles?
• How to learn these preferences and profiles from
  episodic logging?
• Novel ideas for open questions
• Collaborative meeting scheduling based on context
  and history
• Acquired preferences in different contexts
• Acquired beliefs of scheduling preferences of
  others
• Determination of profiles for management
• Use of learned profiles to overcome user burden
  managing calendar
• Direct, closed loop integration with user email
  stream

20
Learning

• Accumulation of episodes
• Control learning State/action models
• Probabilistic dependencies
• Statistical strategy selection
• Multiagent learning

21
The Impact

• Scientific advances
• Continuous mixed-initiative scheduling
• Multi-threaded process management and logging
• Learning of interaction preferences and profiles
• Seamless integration of scheduler, manager,
  learner
• Performance
• Full implementation RADAR improves users
  activity
• GEMs Generalized modules for similar activity
  management extend to space task

22
The Plan

• Next Steps
• Collecting data from the team
• Templates as a stub for email extractor
• Representation of scheduling preferences and
  profiles
• Assemble architecture
• Scheduler, manager, knowledge base, user, learner
• Scheduling engine
• Logging of scheduling process
• Long Run
• Learning over collected data
• Development of protocols and algorithms for
  distributed resolution of scheduling conflicts
• Multiagent collaboration and sharing among EPCAs