Watch what I do: programming by demonstration
Watch what I do: programming by demonstration
Version Space Algebra and its Application to Programming by Demonstration
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Input-output HMMs for sequence processing
IEEE Transactions on Neural Networks
DocWizards: a system for authoring follow-me documentation wizards
Proceedings of the 18th annual ACM symposium on User interface software and technology
Augmentation-based learning: combining observations and user edits for programming-by-demonstration
Proceedings of the 11th international conference on Intelligent user interfaces
Proceedings of the 12th international conference on Intelligent user interfaces
Sheepdog, parallel collaborative programming-by-demonstration
Knowledge-Based Systems
Typed linear chain conditional random fields and their application to intrusion detection
IDEAL'10 Proceedings of the 11th international conference on Intelligent data engineering and automated learning
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We present a novel approach to learning predictive sequential models, called similarity-based alignment and generalization, which incorporates in the induction process a specific form of domain knowledge derived from a similarity function between the points in the input space. When applied to Hidden Markov Models, our framework yields a new class of learning algorithms called SimAlignGen. We discuss the application of our approach to the problem of programming by demonstration–the problem of learning a procedural model of user behavior by observing the interaction an application Graphical User Interface (GUI). We describe in detail the SimIOHMM, a specific instance of SimAlignGen that extends the known Input-Output Hidden Markov Model (IOHMM). Empirical evaluations of the SimIOHMM show the dependence of the prediction accuracy on the introduced similarity bias, and the computational gains over the IOHMM.