Deriving expectations to guide knowledge base creation
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Toward conversational human-computer interaction
AI Magazine
Mechanical transformation of task heuristics into operational procedures
Mechanical transformation of task heuristics into operational procedures
Sheepdog: learning procedures for technical support
Proceedings of the 9th international conference on Intelligent user interfaces
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
Journal of Artificial Intelligence Research
Integrating expectations from different sources to help end users acquire procedural knowledge
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Recovering from errors during programming by demonstration
Proceedings of the 13th international conference on Intelligent user interfaces
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Many useful planning tasks are handled by plan execution tools, such as PRS, that expand procedure definitions and keep track of several interacting goals and tasks. Learning by instruction is a promising approach to help users modifY the definitions of the procedures. However, the impact of the set of possible instructions on the performance of such systems is not well understood. We develop a framework in which instruction templates may be characterized in terms of syntactic transforms on task definitions, and use it to explore the properties of coverage, ambiguity and efficiency in the set of instructions that are understood by an implemented task learning system. We determine what kind of ambiguity is affected by the instruction set, and show how context-dependent interpretation can increase efficiency and coverage without increasing ambiguity.