Integrating Probabilistic Reasoning into a Symbolic Diagrammatic Reasoner

Joseph L. Bokor and Ronald W. Ferguson, Georgia Institute of Technology

A key part of diagram understanding is the problem of glyph recognition. Glyph recognition is hard, because a glyph may be drawn in many different ways and with varying levels of precision. A diagrammatic reasoner must be able to recognize such glyphs. This paper presents a new glyph recognition mechanism that combines a probabilistic representation with an existing symbolic diagrammatic reasoner. This reasoner, GeoRep, recognizes glyphs using a visual domain theory supported by a logic-based truth-maintenance system (LTMS). Here we extend GeoRep’s LTMS to include nodes that encapsulate naive Bayes classifiers. The result is a reasoner that can leverage the benefits of both symbolic truth maintenance and probabilistic networks.


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