ACM Transactions on Computer-Human Interaction (TOCHI)
Getting more out of programming-by-demonstration
Proceedings of the SIGCHI conference on Human Factors in Computing Systems
Proceedings of the 8th international conference on Intelligent user interfaces
Dependency networks for inference, collaborative filtering, and data visualization
The Journal of Machine Learning Research
Training conditional random fields via gradient tree boosting
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Machine Learning
Text clustering with extended user feedback
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Top-down induction of first order logical decision trees
AI Communications
Relational Dependency Networks
The Journal of Machine Learning Research
Recovering from errors during programming by demonstration
Proceedings of the 13th international conference on Intelligent user interfaces
Interacting meaningfully with machine learning systems: Three experiments
International Journal of Human-Computer Studies
ILP'10 Proceedings of the 20th international conference on Inductive logic programming
TildeCRF: conditional random fields for logical sequences
ECML'06 Proceedings of the 17th European conference on Machine Learning
Hi-index | 0.00 |
Some supervised-learning algorithms can make effective use of domain knowledge in addition to the input-output pairs commonly used in machine learning. However, formulating this additional information often requires an in-depth understanding of the specific knowledge representation used by a given learning algorithm. The requirement to use a formal knowledge-representation language means that most domain experts will not be able to articulate their expertise, even when a learning algorithm is capable of exploiting such valuable information. We investigate a method to ease this knowledge acquisition through the use of a graphical, human-computer interface. Our interface allows users to easily provide advice about specific examples, rather than requiring them to provide general rules; we leave the task of properly generalizing such advice to the learning algorithms. We demonstrate the effectiveness of our approach using the Wargus real-time strategy game, comparing learning with no advice to learning with concrete advice provided through our interface, as well as comparing to using generalized advice written by an AI expert. Our results show that our approach of combining a GUI-based advice language with an advice-taking learning algorithm is an effective way to capture domain knowledge.