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Learning to describe data in actions

Abstract
Traditional machine learning algorithms have failed to serve the needs of systems for Programming by Demonstration (PBD), which require interaction with a user (a teacher) and a task environment. We argue that traditional learning algorithms fail for two reasons: they do not cope with the ambiguous instructions that users provide in addition to examples; and their learning criterion requires only that concepts classify examples to some degree of accuracy, ignoring the other ways in which an active agent might use concepts. We show how a classic concept learning algorithm can be adapted for use in PBD by replacing the learning criterion with a set of instructional and utility criteria, and by replacing a statistical preference bias with a set of heuristics that exploit user hints and background knowledge to focus attention.
Type
Conference Contribution
Type of thesis
Series
Citation
Maulsby, D. & Witten, I.H. (1995). Learning to describe data in actions. In Proceedings of Workshop on Programming by Demonstration, Twelfth International Conference on Machine Learning, Lake Tahoe, USA, July 9th 1995 (pp. 65-73).
Date
1995
Publisher
Degree
Supervisors
Rights
This article has been published in Proceedings of Twelfth International Conference on Machine Learning, Lake Tahoe, USA, July 9th 1995. © 1995 the authors.