Applying machine learning to programming by demonstration

Authors: Gordon W. Paynter; Ian H. Witten

Source: Journal of Experimental & Theoretical Artificial Intelligence, Volume 16, Number 3, July-September, 2004 , pp. 161-188(28)

Publisher: Taylor and Francis Ltd

Buy & download fulltext article:

Price: $56.94 plus tax (Refund Policy)

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.

Keywords: programming by demonstration; adaptive user interfaces; human-computer interaction; machine learning; iterative tasks

Document Type: Research article

DOI: http://dx.doi.org/10.1080/09528130412331290520

Publication date: 2004-07-01

More about this publication?