user-initiated learning for assistive interfaces user-initiated learning motivation all learning...

User-Initiated Learning for Assistive Interfaces USER-INITIATED LEARNING Motivation All learning tasks are pre- defined before deployment The learning components are carefully hand-tuned by machine learning experts Proposed Idea Empower the end-users to define new learning tasks without a machine learning expert CALO autonomously formulates and solves the learning problem An Example Scenario User asks CALO to learn to predict whether I intend to set sensitivity of an outgoing email CALO collects training examples for this task and learns to predict sensitivity CALO reminds the user whenever he forgets to set sensitivity EXPERIMENTS Problem Attachment Prediction Data Set Emails obtained from a real user SUMMARY AND FUTURE WORK Summary A prototype functionality was developed that allows a user to define new learning tasks Experiments show that self-tuning of parameters is important for successful learning Systems that allow the users to guide learning is a possibility Future Work Natural interface for the user to guide learning: • create learning tasks • give advice (advice on relational features?) • examine performance • provide feedback (improve advice) Newer algorithms that incorporate advice: • learn from good advice • resist bad advice CALO should notice when it could help the user by formulating and solving new learning tasks. LEARNING Learning Algorithm Logistic Regression chosen as the core learning algorithm Features Relational features extracted from ontology Incorporate User Advice on Features Apply large prior variance on user selected features Select prior variance on rest of the features through cross-validation Automated Model Selection Parameters: Prior variance on weights, classification threshold Technique: Maximization of leave-one-out cross- validation estimate of kappa user Instrumented Outlook Integrated Task Learning SAT Based Reasoning System Modify Procedure Compose New email User Interface for Feature Guidance Feature Guidance Email + Related Objects Ontology Machine Learner Knowledge Base Training Examples Events SPARK Procedure Legal Features User Selected Features Instrumented Outlook Trained Classifier Assistant SAT Based Reasoning System user Compose New Email New Email New Email Prediction Forgot? Prediction And Email Forgot = False Send Email Forgot = True Remind user user ARCHITECTURE Procedure Demonstration Learning Task Creation Feature Guidance Learning Assistance Learning Configurations Compared No User Advice + Fixed Model Parameters User Advice + Fixed Model Parameters No User Advice + Model Selection User Advice + Model Selection UIL ACTIVITY FLOW UIL EXPERIENCE Kshitij Judah, Jim Blythe, Oliver Brdiczka, Thomas Dietterich, Christopher Ellwood, Melinda Gervasio, Jed Irvine, Bill Jarrold, Michael Slater, Prasad Tadepalli, Jim Thornton, Alan Fern IRIS CALO DESKTOP PLUGINS EXTERNAL APPLICATIONS Integrated Task Learning Instrumented Outlook UIL SAT Based Reasoning System User Interface for Feature Guidance Machine Learner in the Box Assistant

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User-Initiated Learning for Assistive InterfacesUSER-INITIATED LEARNING Motivation All learning tasks are pre-defined

before deploymentThe learning components are carefully

hand-tuned by machine learning expertsProposed Idea Empower the end-users to define new

learning tasks without a machine learning expert

CALO autonomously formulates and solves the learning problem

An Example Scenario User asks CALO to learn to predict

whether I intend to set sensitivity of an outgoing email

CALO collects training examples for this task and learns to predict sensitivity

CALO reminds the user whenever he forgets to set sensitivity

EXPERIMENTS Problem Attachment Prediction Data Set Emails obtained from a real user

SUMMARY AND FUTURE WORK Summary A prototype functionality was developed that

allows a user to define new learning tasks Experiments show that self-tuning of parameters

is important for successful learning Systems that allow the users to guide learning is

a possibility

Future Work Natural interface for the user to guide learning:• create learning tasks• give advice (advice on relational features?)• examine performance• provide feedback (improve advice)

Newer algorithms that incorporate advice:• learn from good advice• resist bad advice

CALO should notice when it could help the user by formulating and solving new learning tasks.

LEARNING Learning AlgorithmLogistic Regression chosen as the

core learning algorithm Features Relational features extracted from

ontology Incorporate User Advice on

Features Apply large prior variance on user

selected features Select prior variance on rest of the

features through cross-validation Automated Model Selection Parameters: Prior variance on

weights, classification threshold Technique: Maximization of leave-

one-out cross-validation estimate of kappa

user

InstrumentedOutlook

Integrated Task Learning

SAT Based Reasoning System

ModifyProcedure

ComposeNewemail

User Interface for Feature Guidance

Feature Guidance

Email + Related Objects

Ontology

Machine Learner

KnowledgeBase

Training Examples

Events SPARKProcedure

LegalFeatures

UserSelectedFeatures

InstrumentedOutlook

Trained Classifier

Assistant

SAT Based Reasoning System

user

ComposeNewEmail

NewEmail

Prediction

Forgot?Prediction

AndEmail

Forgot = False

SendEmail

Forgot =

True

Remind

user user

ARCHITECTURE

Procedure Demonstration Learning Task Creation Feature Guidance Learning Assistance

Learning Configurations Compared No User Advice + Fixed Model Parameters User Advice + Fixed Model Parameters No User Advice + Model Selection User Advice + Model Selection

UIL ACTIVITY FLOW

UIL EXPERIENCE

Kshitij Judah, Jim Blythe, Oliver Brdiczka, Thomas Dietterich, Christopher Ellwood, Melinda Gervasio, Jed Irvine, Bill Jarrold, Michael Slater, Prasad Tadepalli, Jim Thornton, Alan Fern

IRIS

CALO DESKTOP

PLUGINS EXTERNAL APPLICATIONS

Integrated Task Learning

InstrumentedOutlook

UIL

SAT Based Reasoning System

User Interface for Feature Guidance

Machine Learner in the Box

Assistant

TARGET Instant Payments Settlement User Requirements · The market consultation on the TARGET Instant Payments Settlement (TIPS) User Requirements Document (URD) is herewith initiated

WO/GA/52/ - wipo.int · Web viewAward initiated by Audit without Walls, a learning community for public sector auditors initiated by the Office of the Auditor General of the Asian