Universal subgoaling and chunking: the automatic generation and learning of goal hierarchies
Universal subgoaling and chunking: the automatic generation and learning of goal hierarchies
TNT: a talking tutor 'n' trainer for teaching use of interactive computer systems
CHI '86 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Machine Learning
Okies: a troubleshooter in the factory
IEA/AIE '88 Proceedings of the 1st international conference on Industrial and engineering applications of artificial intelligence and expert systems - Volume 1
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Conceptual clustering enhances the value of existing databases by revealing patterns in the data. These patterns may be useful for understanding trends, making predictions of future events from historical data, or synthesizing data records into meaningful clusters.LODE (Learning On Database Environments) is an incremental conceptual clustering program. The premise of the LODE system is that the task of discovering patterns in a large set of potentially noisy examples can be accomplished in a generate and test paradigm using generalization techniques to generate hypotheses describing similar examples and then testing the accuracy of these hypotheses by comparing them to examples. The LODE system is an implementation of this premise.LODE was used to analyze keystroke data collected from novices learning to use the vi editor. The analysis shows that LODE discovered descriptions of recurring patterns of errors made by the novices that are known as mode errors.