Learning one subprocedure per lesson
Artificial Intelligence
Programming by demonstration: an inductive learning formulation
IUI '99 Proceedings of the 4th international conference on Intelligent user interfaces
RoadRunner: Towards Automatic Data Extraction from Large Web Sites
Proceedings of the 27th International Conference on Very Large Data Bases
Extracting structured data from Web pages
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
WebTables: exploring the power of tables on the web
Proceedings of the VLDB Endowment
A computational model of accelerated future learning through feature recognition
ITS'10 Proceedings of the 10th international conference on Intelligent Tutoring Systems - Volume Part II
Problem order implications for learning transfer
ITS'12 Proceedings of the 11th international conference on Intelligent Tutoring Systems
Efficient cross-domain learning of complex skills
ITS'12 Proceedings of the 11th international conference on Intelligent Tutoring Systems
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People learn to read and understand various displays (e.g., tables on webpages and software user interfaces) every day. How do humans learn to process such displays? Can computers be efficiently taught to understand and use such displays? In this paper, we use statistical learning to model how humans learn to perceive visual displays. We extend an existing probabilistic context-free grammar learner to support learning within a two-dimensional space by incorporating spatial and temporal information. Experimental results in both synthetic domains and real world domains show that the proposed learning algorithm is effective in acquiring user interface layout. Furthermore, we evaluate the effectiveness of the proposed algorithm within an intelligent tutoring agent, SimStudent, by integrating the learned display representation into the agent. Experimental results in learning complex problem solving skills in three domains show that the learned display representation is as good as one created by a human expert, in that skill learning using the learned representation is as effective as using a manually created representation.