Machine Generalization and Human Categorization: An Information-Theoretic View
James Corter, Mark Gluck
In designing an intelligent system that must be able to explain its reasoning to a human user, or to provide generalizations that the human user finds reasonable, it may be useful to take into consideration psychological data on what types of concepts and categories people naturally use. The psychological literature on concept learning and categorization provides strong evidence that certain categories are more easily learned, recalled, and recognized than others. We show here how a measure of the informational value of a category predicts the results of several important categorization experiments better than standard alternative explanations. This suggests that information-based approaches to machine generalization may prove particularly useful and natural for human users of the systems.
PDF Link: /papers/85/p201-corter.pdf
AUTHOR = "James Corter
and Mark Gluck",
TITLE = "Machine Generalization and Human Categorization: An Information-Theoretic View",
BOOKTITLE = "Proceedings of the First Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-85)",
PUBLISHER = "AUAI Press",
ADDRESS = "Corvallis, Oregon",
YEAR = "1985",
PAGES = "201--207"