User-Initiated Learning for Assistive Interfaces

1. CALO DESKTOP User-Initiated Learning for Assistive Interfaces Ontology EXTERNAL APPLICATIONS PLUGINS UIL SAT Based Reasoning System • 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 • 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 • 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. Integrated Task Learning ARCHITECTURE User Interface for Feature Guidance • Learning Configurations Compared • No User Advice + Fixed Model Parameters • User Advice + Fixed Model Parameters • No User Advice + Model Selection • User Advice + Model Selection user Instrumented Outlook Machine Learner in the Box Assistant IRIS User Selected Features New Email Forgot = False SPARK Procedure Legal Features Events Prediction Trained Classifier Email + Related Objects UIL ACTIVITY FLOW Instrumented Outlook Integrated Task Learning SAT Based Reasoning System Machine Learner Instrumented Outlook User Interface for Feature Guidance Assistant Send Email New Email Prediction And Email Compose New Email Compose New email Feature Guidance Forgot? Modify Procedure Training Examples Forgot = True SAT Based Reasoning System Remind Knowledge Base user user user 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