SALT: a knowledge acquisition language for propose-and-revise systems
Artificial Intelligence
KADS: a modelling approach to knowledge engineering
Knowledge Acquisition - Special issue on the KADS approach to knowledge engineering
Knowledge refinement in a reflective architecture
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Task modeling with reusable problem-solving methods
Artificial Intelligence
The Knowledge Acquisition and Representation Language, KARL
IEEE Transactions on Knowledge and Data Engineering
Building concept representations from reusable components
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Tools for assembling modular ontologies in ontolingua
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Explicit representations of problem-solving strategies to support knowledge acquisition
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
User studies of an interdependency-based interface for acquiring problem-solving knowledge
Proceedings of the 5th international conference on Intelligent user interfaces
An integrated environment for knowledge acquisition
Proceedings of the 6th international conference on Intelligent user interfaces
Deriving Acquisition Principles from Tutoring Principles
ITS '02 Proceedings of the 6th International Conference on Intelligent Tutoring Systems
Task learning by instruction in tailor
Proceedings of the 10th international conference on Intelligent user interfaces
Incorporating tutoring principles into interactive knowledge acquisition
International Journal of Human-Computer Studies
Case-based reasoning for procedure learning by instruction
Proceedings of the 13th international conference on Intelligent user interfaces
Automatic interpretation of loosely encoded input
Artificial Intelligence
Interpreting loosely encoded questions
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
An analysis of procedure learning by instruction
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
Knowledge analysis on process models
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
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
Review: interactive knowledge capture in the new millennium: How the semantic web changed everything
The Knowledge Engineering Review
Adaptable methodology for automation application development
Advanced Engineering Informatics
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Successful approaches to developing knowledge acquisition tools use expectations of whatthe user has to add or may want to add, based on how new knowledge fits within a knowledge base that already exists. When a knowledge base is first created or undergoes significant extensions and changes, these tools cannot provide much support. This paper presents an approach to creating expectations when a new knowledge base is built, and describes a knowledge acquisition tool that we implemented using this approach that supports users in creating problem-solving knowledge. As the knowledge base grows, the knowledge acquisition tool derives more frequent and more reliable expectations that result from enforcing constraints in the knowledge representation system, looking for missing pieces of knowledge in the knowledge base, and working out incrementally the interdependencies among the different components of the knowledge base. Our preliminary evaluations show a thirty percent time savings during knowledge acquisition. Moreover, by providing tools to support the initial phases of knowledge base development, many mistakes are detected early on and even avoided altogether. We believe that our approach contributes to improving the quality of the knowledge acquisition process and of the resulting knowledge-based systems as well.