Watch what I do: programming by demonstration
Watch what I do: programming by demonstration
Learning hierarchical task models by defining and refining examples
Proceedings of the 1st international conference on Knowledge capture
Predicting UNIX Command Lines: Adjusting to User Patterns
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Programming by Demonstration Using Version Space Algebra
Machine Learning
Learning programs from traces using version space algebra
Proceedings of the 2nd international conference on Knowledge capture
Input-output HMMs for sequence processing
IEEE Transactions on Neural Networks
Task learning by instruction in tailor
Proceedings of the 10th international conference on Intelligent user interfaces
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 SIGCHI Conference on Human Factors in Computing Systems
Proceedings of the 12th international conference on Intelligent user interfaces
Building data integration queries by demonstration
Proceedings of the 12th international conference on Intelligent user interfaces
Graphstract: minimal graphical help for computers
Proceedings of the 20th annual ACM symposium on User interface software and technology
Recovering from errors during programming by demonstration
Proceedings of the 13th international conference on Intelligent user interfaces
Case-based reasoning for procedure learning by instruction
Proceedings of the 13th international conference on Intelligent user interfaces
Generating photo manipulation tutorials by demonstration
ACM SIGGRAPH 2009 papers
An analysis of procedure learning by instruction
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
PLOW: a collaborative task learning agent
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Interpreting written how-to instructions
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Lowering the barriers to website testing with CoTester
Proceedings of the 15th international conference on Intelligent user interfaces
Sheepdog, parallel collaborative programming-by-demonstration
Knowledge-Based Systems
A new representation and associated algorithms for generalized planning
Artificial Intelligence
How to serve soup: interleaving demonstration and assisted editing to support nonprogrammers
Proceedings of the 16th international conference on Intelligent user interfaces
Building Mashups by Demonstration
ACM Transactions on the Web (TWEB)
ACM Transactions on Graphics (TOG)
Creating contextual help for GUIs using screenshots
Proceedings of the 24th annual ACM symposium on User interface software and technology
The impact of tutorials on games of varying complexity
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
LiveAction: Automating Web Task Model Generation
ACM Transactions on Interactive Intelligent Systems (TiiS)
Hint systems may negatively impact performance in educational games
Proceedings of the first ACM conference on Learning @ scale conference
| Hi-index | 0.00 |
Technical support procedures are typically very complex. Users often have trouble following printed instructions describing how to perform these procedures, and these instructions are difficult for support personnel to author clearly. Our goal is to learn these procedures by demonstration, watching multiple experts performing the same procedure across different operating conditions, and produce an executable procedure that runs interactively on the user's desktop. Most previous programming by demonstration systems have focused on simple programs with regular structure, such as loops with fixed-length bodies. In contrast, our system induces complex procedure structure by aligning multiple execution traces covering different paths through the procedure. This paper presents a solution to this alignment problem using Input/Output Hidden Markov Models. We describe the results of a user study that examines how users follow printed directions. We present Sheepdog, an implemented system for capturing, learning, and playing back technical support procedures on the Windows desktop. Finally, we empirically evalute our system using traces gathered from the user study and show that we are able to achieve 73% accuracy on a network configuration task using a procedure trained by non-experts.