Watch what I do: programming by demonstration
Watch what I do: programming by demonstration
Contextual design: defining customer-centered systems
Contextual design: defining customer-centered systems
A library of generic concepts for composing knowledge bases
Proceedings of the 1st international conference on Knowledge capture
Exploiting Local Similarity for Indexing Paths in Graph-Structured Data
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 2
Task learning by instruction in tailor
Proceedings of the 10th 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
PLOW: a collaborative task learning agent
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
POIROT: integrated learning of web service procedures
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
Journal of Artificial Intelligence Research
No Code Required: Giving Users Tools to Transform the Web
No Code Required: Giving Users Tools to Transform the Web
RECYCLE: Learning looping workflows from annotated traces
ACM Transactions on Intelligent Systems and Technology (TIST)
Virtual butler: what can we learn from adaptive user interfaces?
Your Virtual Butler
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Recent years have seen a resurgence of interest in programming by demonstration. As end users have become increasingly sophisticated, computer and artificial intelligence technology has also matured, making it feasible for end users to teach long, complex procedures. This paper addresses the problem of learning from demonstrations involving unobservable (e.g., mental) actions. We explore the use of knowledge base inference to complete missing dataflow and investigate the approach in the context of the CALO cognitive personal desktop assistant. We experiment with the Pathfinder utility, which efficiently finds all the relationships between any two objects in the CALO knowledge base. Pathfinder often returns too many paths to present to the user and its default shortest path heuristic sometimes fails to identify the correct path. We develop a set of filtering techniques for narrowing down the results returned by Pathfinder and present experimental results showing that these techniques effectively reduce the alternative paths to a small, meaningful set suitable for presentation to a user.