Watch what I do: programming by demonstration
Watch what I do: programming by demonstration
Repeat and predict—two keys to efficient text editing
CHI '94 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Acquisition of abstract plan descriptions for plan recognition
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
An integrated shell and methodology for rapid development of knowledge-based agents
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
Using plan recognition in human-computer collaboration
UM '99 Proceedings of the seventh international conference on User modeling
Your wish is my command: programming by example
Your wish is my command: programming by example
COLLAGEN: A Collaboration Manager for Software Interface Agents
User Modeling and User-Adapted Interaction
Developing task models from informal scenarios
CHI '99 Extended Abstracts on Human Factors in Computing Systems
CTTE: an environment for analysis and development of task models of cooperative applications
CHI '01 Extended Abstracts on Human Factors in Computing Systems
Learning Hierarchical Performance Knowledge by Observation
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Version Space Algebra and its Application to Programming by Demonstration
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
From Interaction Data to Plan Libraries: A Clustering Approach
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Acquiring Problem-Solving Knowledge from End Users: Putting Interdependency Models to the Test
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Learning what to instruct: acquiring knowledge from demonstrations and focussed experimentation
Learning what to instruct: acquiring knowledge from demonstrations and focussed experimentation
A collaborative planning model of intentional structure
Computational Linguistics
Explicit representations of problem-solving strategies to support knowledge acquisition
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Agents and GUIs from task models
Proceedings of the 7th international conference on Intelligent user interfaces
Sheepdog: learning procedures for technical support
Proceedings of the 9th international conference on Intelligent user interfaces
Learning approximate preconditions for methods in hierarchical plans
ICML '05 Proceedings of the 22nd international conference on Machine learning
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
No Code Required: Giving Users Tools to Transform the Web
No Code Required: Giving Users Tools to Transform the Web
LiveAction: Automating Web Task Model Generation
ACM Transactions on Interactive Intelligent Systems (TiiS)
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Task models are used in many areas of computer science including planning, intelligent tutoring, plan recognition, interface design, and decision theory. However, developing task models is a significant practical challenge. We present a task model development environment centered around a machine learning engine that infers task models from examples. A novel aspect of the environment is support for a domain expert to refine past examples as he or she develops a clearer understanding of how to model the domain. Collectively, these examples constitute a "test suite" that the development environment manages in order to verify that changes to the evolving task model do not have unintended consequences.