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Representing quantitative and qualitative knowledge in a knowledge-based storm-forecasting system

Authors
Journal
International Journal of Man-Machine Studies
0020-7373
Publisher
Elsevier
Publication Date
Volume
25
Issue
5
Identifiers
DOI: 10.1016/s0020-7373(86)80021-9
Disciplines
  • Earth Science
  • Logic

Abstract

Abstract METEOR is a rule- and frame-based system for short-term severe storm forecasting. Initial predictions are based on interpretations of contour maps generated by statistical predictors of storm severity. To confirm these predictions, METEOR considers additional quantitative measurements, ongoing meteorological conditions and events, and how the expert forecaster interprets these factors. Meterorological events are derived from interpreting human observations of weather conditions in the forecast area. This task requires a framework that supports inferences about the temporal and spatial features of meteorological activities. To accommodate the large amounts of different types of knowledge characterizing this problem, a number of extensions to the rule and frame representations were developed. These extensions include a view scheme to direct property inheritance through intermingled hierarchies and the automatic generation of production system rules from descriptions stored in frames on an as-needed basis.

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