Toward harnessing user feedback for machine learning
Proceedings of the 12th international conference on Intelligent user interfaces
Applying common sense to distance learning: the case of home care education
IHC '06 Proceedings of VII Brazilian symposium on Human factors in computing systems
Interacting meaningfully with machine learning systems: Three experiments
International Journal of Human-Computer Studies
A common sense-based on-line assistant for training employees
INTERACT'07 Proceedings of the 11th IFIP TC 13 international conference on Human-computer interaction
Hi-index | 0.01 |
One of the principal problems of online help is the mismatch between the specialized knowledge and technical vocabulary of experts who are providing the help, and the relative naïveté of novices, who usually are often not in a position to understand solutions expressed by the expert in their own terms. Most of the interfaces are plagued by recurrent key problems: 1) elicitation - how to ask questions that enable the helper to make decisions, and at the same time, are understandable to the novice, and 2) explanation -- how to explain rationale behind expert decisions in terms that the user can understand. One of the best ways to do this is for the expert to provide analogies in terms of Commonsense knowledge, which provide metaphors that help novices learn problem-solving skills. SuggestDesk is a system that acts as an advisor to an online technical support person. It uses a large Commonsense knowledge base to search for analogies between known technical problem-solution pairs, and situations and events in everyday life that can be used to explain them.