Improving Generalization with Active Learning
Machine Learning - Special issue on structured connectionist systems
Selective Sampling Using the Query by Committee Algorithm
Machine Learning
The FindMe Approach to Assisted Browsing
IEEE Expert: Intelligent Systems and Their Applications
Machine Learning
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
A selective sampling approach to active feature selection
Artificial Intelligence
Verbosity: a game for collecting common-sense facts
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Hybrid critiquing-based recommender systems
Proceedings of the 12th international conference on Intelligent user interfaces
Active Learning with Feedback on Features and Instances
The Journal of Machine Learning Research
Democratic approximation of lexicographic preference models
Proceedings of the 25th international conference on Machine learning
Proactive learning: cost-sensitive active learning with multiple imperfect oracles
Proceedings of the 17th ACM conference on Information and knowledge management
An interface for targeted collection of common sense knowledge using a mixture model
Proceedings of the 14th international conference on Intelligent user interfaces
A comparative user study on rating vs. personality quiz based preference elicitation methods
Proceedings of the 14th international conference on Intelligent user interfaces
POIROT: integrated learning of web service procedures
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
Anytime induction of low-cost, low-error classifiers: a sampling-based approach
Journal of Artificial Intelligence Research
Designing robot learners that ask good questions
HRI '12 Proceedings of the seventh annual ACM/IEEE international conference on Human-Robot Interaction
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Intelligent systems require substantial bodies of problem-solving knowledge. Machine learning techniques hold much appeal for acquiring such knowledge but typically require extensive amounts of user-supplied training data. Alternatively, informed question asking can supplement machine learning by directly eliciting critical knowledge from a user. Question asking can reduce the amount of training data required, and hence the burden on the user; furthermore, focused question asking holds significant promise for faster and more accurate acquisition of knowledge. In previous work, we developed static strategies for question asking that provide background knowledge for a base learner, enabling the learner to make useful generalizations even with few training examples. Here, we extend that work with a learning approach for automatically acquiring question-asking strategies that better accommodate the interdependent nature of questions. We present experiments validating the approach and showing its usefulness for acquiring efficient, context-dependent question-asking strategies.