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dc.contributor.authorPaynter, Gordon W.
dc.contributor.authorWitten, Ian H.
dc.contributor.authorKoblitz, Neil
dc.contributor.authorPowell, Matthew
dc.identifier.citationPaynter, G. W. & Witten, I.H. (2004). Applying machine learning to programming by demonstration. Joutnal of Experimental & Theoretical Artificial Intelligence, 16(3), 161-188.en
dc.description.abstract‘Familiar’ is a tool that helps end-users automate iterative tasks in their applications by showing examples of what they want to do. It observes the user’s actions, predicts what they will do next, and then offers to complete their task. Familiar learns in two ways. First, it creates a model, based on data gathered from training tasks, that selects the best prediction from among several candidates. Experiments show that decision trees outperform heuristic methods, and can be further improved by incrementally updating the classifier at task time. Second, it uses decision stumps inferred from analogous examples in the event trace to predict the parameters of conditional rules. Because data is sparse—for most users balk at giving more than a few training examples—permutation tests are used to calculate the statistical significance of each stump, successfully eliminating bias towards attributes with many different values.en
dc.publisherTaylor & Francisen
dc.subjectcomputer scienceen
dc.subjectprogramming by demonstrationen
dc.subjectadaptive user interfacesen
dc.subjecthuman-computer interactionen
dc.subjectmachine learningen
dc.subjectiterative tasks
dc.titleApplying machine learning to programming by demonstrationen
dc.typeJournal Articleen
dc.relation.isPartOfJournal of Experimental & Theoretical Artificial Intelligenceen_NZ

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