EAGER: programming repetitive tasks by example
CHI '91 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
SmallStar: programming by demonstration in the desktop metaphor
Watch what I do
Domain-independent programming by demonstration in existing applications
Your wish is my command
Version Space Algebra and its Application to Programming by Demonstration
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Sheepdog: learning procedures for technical support
Proceedings of the 9th international conference on Intelligent user interfaces
Estimating the Numbers of End Users and End User Programmers
VLHCC '05 Proceedings of the 2005 IEEE Symposium on Visual Languages and Human-Centric Computing
Augmentation-based learning: combining observations and user edits for programming-by-demonstration
Proceedings of the 11th international conference on Intelligent user interfaces
Koala: capture, share, automate, personalize business processes on the web
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Recovering from errors during programming by demonstration
Proceedings of the 13th international conference on Intelligent user interfaces
From geek to sleek: integrating task learning tools to support end users in real-world applications
Proceedings of the 14th international conference on Intelligent user interfaces
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The Adept Task Learning system is an end-user programming environment that combines programming by demonstration and direct manipulation to support customization by nonprogrammers. Previously, Adept enforced a rigid procedure-authoring workflow consisting of demonstration followed by editing. However, a series of system evaluations with end users revealed a desire for more feedback during learning and more flexibility in authoring. We present a new approach that interleaves incremental learning from demonstration and assisted editing to provide users with a more flexible procedure-authoring experience. The approach relies on maintaining a "soup" of alternative hypotheses during learning, propagating user edits through the soup, and suggesting repairs as needed. We discuss the learning and reasoning techniques that support the new approach and identify the unique interaction design challenges they raise, concluding with an evaluation plan to resolve the design challenges and complete the improved system.