CALO Learning Overview

CALO Learning Overview

AIC Machine Learning Discussion Group

26 October 2004

with material shamelessly pilfered from previous presentations by:

• Tom Dietterich/Leslie Kaelbling (Transfer Learning)• Colin Evans (Task Setup)• Lynn Voss (Task Discussion)• David Martin (Task Fulfillment)

The Learning Picture (Terminology)

learningalgorithm

learneddevice

naïve Bayesmaximum entropyC4.5k-meanslearning by being told…

Bayesian networkHMMdecision treeinformation extractorprocedureclusterer…

predicted categoriesranked listsfacts/relationssocial networksclusters…

(test) instancecurrent statedocument corpusprior knowledge…

labeled training setannotated corpusexecution traces…

algorithminput

deviceinput

deviceoutput

meta-learningalgorithm

ML Today: “Engineered” Learning

data set

learningalgorithm

learneddevice

algorithminput

deviceinput

deviceoutput

human engineers features,invents algorithms, runs experimentsto find the best performance on(static) data sets

The Vision: Learning in the Wild

learningalgorithm

learneddevice

algorithminput

deviceinput

deviceoutput

system decides when to learn, what to learn, and how to learn, and adapts itself through interaction with the environment

ENVIRONMENT

The Vision Behind the Vision: Robust, Enduring Systems

CALO learns toperform Task A

CALOimmediately performs

Task B better

CALO learns toperform Task B

faster

transfer learning

Learned on Task BLearned on Task A

Example 1: Transfer of Learned Facts

Task A: Meeting Planning

Who should attend budget meeting for Project X?

Task B: Purchasing

Who can approve purchases on Project X?

Financial officers should attend budget meetings

Stephen Q. is financial officer for Project X

Financial officers can approve purchases

Stephen Q. should attend budget meeting

Stephen Q. can approve purchases

Transfer Learning, Tom Dietterich & Leslie Kaelbling

Example 2:Transfer of Learned Subprocedures

Task A: Purchasing Computers Task B: Purchasing Books

Tradeoff Specs, Price, Availability

ComputerMeetsSpecs

AvailabilityShipping Cost

Tradeoff Specs, Price, Availability

BookMeetsSpecs

AvailabilityShipping Cost

Computer Specs:• CPU speed • Memory size • Disk size

Book Specs:• Title • Author • Binding

Availability:• Discontinued • Back ordered • Delivery date

Availability:• Out of print • Back ordered • Delivery date


Example 3:Transfer of Learned Ontology

Task A: Tenure review in university Task B: Command and control in Air Force

Leader

Leader Leader

Leader LeaderLeader Leader

Member

Member

Member

Member

Member

Member

Member

Member

Member

Member

Member

Member

Organization is a hierarchy of groups

Each group has a team leader and team members

The members of all groups except the lowest are the team leaders of subgroups

Organization is a hierarchy of groups

Each group has a team leader and team members

The members of all groups except the lowest are the team leaders of subgroups

Tenure dossier flows up hierarchy Orders flow down hierarchy

Note: Domain facts and procedures do NOT transfer:

Leader

Leader Leader

Leader LeaderLeader Leader

Member

Member

Member

Member

Member

Member

Member

Member

Member

Member

Member

Member


Example 4:Transfer of Learned Feature Relevance

Task A: Routing Complaints Task B: Meeting Scheduling

Job title determines job responsibilities

Carpenter: framing, installing cabinetsDrywaller: taping, sealing, texturingPainter: masking, paintingContractor: scheduling, project planning

Job title determines job responsibilities

“Chief Evangelist” might be able to substitute for “Evangelist” in meeting

These inferences can be made without even knowing what “sealing” or

“Evangelist” mean


CALO Organization

• Technology Focus Centers (TFCs)• Reasoning & Action (RA)• Cyber Awareness (CA)• Physical Awareness (PA)• Multi-Modal Dialogue (MMD)• Learning (L)

• Scenarios (Year 1)• meeting scheduling• meeting understanding• laptop purchase

Functional Columns (Year 2)

• Task Setup• recognize implications of starting a new task

• information harvesting, scheduling setup, dossier preparation

• Task Discussion• integrate results of interaction between humans

and CALOs into task management• meeting understanding

• Task Fulfillment• support user in performing tasks

• scheduling, procurement

Task Setup: Information Harvesting

Task Setup, Colin Evans

Learning in Task Setup:Information Harvesting

Component Algorithm Input Device Output

SemEx desktop contents

extraction rules

facts about and relations between documents, email, etc.

DEX home pages

information extractor (CRF)

information about people, including contact information, areas of expertise, and social groups

Activity Clusterer

bi-directional clustering

emailbox activity (task) groupings of email

Task Setup: Scheduling Setup


Learning in Task Setup:Scheduling Setup


SpeechAct email message

email type classifier

email type

Activity Classifier

email message

activity classifier activity of message

emailME email message

information extractor

meeting constraints

Task Manager advice policies for determining meeting constraints (e.g., start time constraints, participants)

Task Setup: Dossier Preparation


Learning in Task Setup:Dossier Preparation


TaskTracer desktop operations

logging relations between tasks (activities) and desktop operations

Naive Bayes

desktop operation

task (activity) classifier

Activity Classifier

email message

activity (task) classifier

activity (task) of message

Task Discussion: Meeting Room

CAMEOCAMEO

Frame

Frame

Fram

e

User w/ headset

User w

/ he

adse

t

User w/ headset

SMART Board

Stereo Camera

Frame includes:•Stereo Camera•(IR - Blue Eyes Camera)•Array Microphones•All attached around a user’s laptop

Task Discussion, Lynn Voss

Task Discussion: Architecture

Meeting Recorder Architecture

Meeting Playback System

Meeting Record / MOKB

Body Tracker3D-Gesture

Face TrackerFace Recog.

Activity Recog.

Speakerlocalization

Affect Recog.

Head, eye,gaze tracker

Object Recog.Video & Array Microphone

Classifiers DialogueManager

Suite

FSDB

MS ProjectAgent

Task Setup

Purchase Request

Tracking DataIntegrator

Meeting DossierAgendaParticipant List

Handwriting2D Gesture

Charter

Digital InkRecognizers

NTP

OAAFacilitator

Raw Data Capture

Audio Server

InstrumentedText Notes &Power Point

CAMEOPanoramic

MPEG encoder

Whiteboard’sStereo

CameraFrame

SMARTBoard

Digital Ink

CloseTalkingSpeech

TasksMilestones

OOV Words

OOVAgentSuite

MS ProjectFile

End Pointer

Transcription

Prosody

TopicSupporting Docs

Meeting Room IRIS Data Store

Meeting Room

IRIS Data Store

UserFeedbackLoop

Multi-parser

MSBITS

Offline Analysis Suite

Agenda Topics

Phases Action Items

Roles Rough Sum.

Tracking DataIntegrator &Audio Server

MeetingBrowser

Task Discussion, Lynn Voss

Learning in Task Discussion:Project Plan Capture


Agent Sphinx

multi-modal learning

speech, gestures, writing

words (complete model)

instruction chart types

2D gesture recognizer


(written) symbols

speech recognizer

speech recognizer speaker ID

handwriting recognizer

handwriting recognizer

words

Learning in Task Discussion:Physical Awareness


CAMEO face recognizer person ID

coarse activity recognizer

sitting, standing, walking, …

BodyTracker articulated tracker participant movement


pointing, orientation

object recognizer objects

affect recognizer affect

Frame speaker localizer speaker location

speaker detection speaker ID

high-level activity recognizer

Learning in Task Discussion:Meeting Awareness


Meeting Analysis Suite

topic tracker topic shifts

agenda tracker agenda segments

action item identifier

action items

decision identifier decisions

meeting phase segmenter

meeting phases

role tracker participant role

acoustic model speech transcript

Task Discussion:Meeting Record Content

• Raw Streams• raw audio, raw video, whiteboard strokes, text notes, PPT

presentations

• Low Level Events• out-of-vocabulary words• participant locations with torso and body positions• participant activities (coarse)• who spoke to whom• recognized affects• recognized words & symbols on the whiteboard• word transcripts• new participants• new chart types• new 2D & 3D gestures

Task Discussion:Meeting Record Content

• High Level Events• project plan (task names, durations, milestones)

• participants, including entrance/exit

• when each agenda item was discussed

• topics/subtopics and relevance to agenda

• action items, including responsible parties, deadlines

• decisions and proposers; alternative proposals and reasons for/against

• participant roles (participator, observer, presenter)

• meeting phases (introductions, discussions, briefings, presentations)

Task Fulfillment: Scheduling

FormulateScheduling

Request(Task Setup)

Gather Information

Prepare Schedule Candidates

Get User Selectionsand/or Confirmations

Update Calendars,Send Notifications

Send Reminders

RelaxScheduling

Request

Task Fulfillment, David Martin

Learning in Task Fulfillment:Scheduling


PLIANT SVM schedule ranker value (cost)

Task Manager

advice policies for scheduling, relaxation, reminder

AutoMinder reinforcement learning

reminder strategy

procedural learner

revision procedure

memory-based learner

case-based learning

scheduling procedure

Task Fulfillment: Purchasing

Select Type of Item

LearnVendors

Choose Vendors &Define Requirements

Get Quotes

Get User Selectionsand/or Confirmation

Refine Purchase ProcedureWrap

VendorsAdd

Vendors

Execute Purchase Procedure

Select Type of Item

RelaxQuery

Task Fulfillment, David Martin

Learning in Task Fulfillment:Purchasing


KnowIt product vendor sites

product ontologies

Fetch learning by being told

Web site Web site wrapper product information

Prometheus Mediator

model of new source

LOQR C4.5 unsatisfiable query

decision rules relaxed query

Tailor learning by being told

revised procedure

Task Manager

advice policies for procurement

The CALO Test

Main Claim: CALO performs well and, through learning, performs even better.

• The Test• AP-style exam• Administered regularly throughout the year• Must show general improvement overall.• Only learning in the wild counts.

The CALO Test

CALO 2.0

CALO 2.1

CALO 2.2

CALO 2.3

CALO 3.0

Tes

t S

core

improvement due to learning

improvement due to engineering

totalimprovement

due toengineeringand learning

Situated Learning

CALO is a cognitive assistant.

• Task Manager (the heartbeat of CALO)• controls what CALO does• situation assessment• workflow management

• Knowledge Machine/Query-Update Manager• what CALO knows• CALO ontology

Situated Learning

CALO is deployed in the office environment.

• IRIS• suite of integrated desktop applications • ontology-driven architecture• provides instrumentation and automation facilities

Learning Issues

CALO is not (yet) a robust, enduring system.

• much in-the-wild learning is not truly online• concept drift/shift is not addressed• disparate sources are not coordinated• new tasks require human engineering• ontology changes require lobotomies• learning is component-specific

CALO Learning Overview

Documents

Transcript of CALO Learning Overview