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

PAL video

Four-minute video:Military-setting motivation for

RADAR (Carnegie Mellon)and CALO (SRI).

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

Year 1: Office allocation

Six-minute video

Automated assignment of offices.

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

Years 2–3: Conference planning

Demo:

Semi-automated schedulingof conference events.

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

Main limitation

We assume that all probability distributions are independent.

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.

• Learning of control rules for high-level planning and elicitation strategies

• Automated selection of reasoning and learning strategies from a library

Future challenges