Integrating Probabilistic Reasoning into a Symbolic Diagrammatic Reasoner
نویسندگان
چکیده
An important part of diagram understanding is the problem of glyph recognition. This problem is challenging because a glyph may be drawn in many different ways and with varying levels of precision. Diagrams in general are rarely perfect and often filled with poorly-drawn glyphs. In order to understand a wide range of diagrams, a diagrammatic reasoner should recognize glyphs that deviate from standard forms. This paper presents a new mechanism for glyph recognition which combines a probabilistic representation with an existing symbolic diagrammatic reasoner. This reasoner, GeoRep, recognizes diagram glyphs using a set of rules supported by a logic-based truth-maintenance system (LTMS). Here we extend GeoRep’s LTMS to include nodes that encapsulate Bayesian networks. The result is a reasoner that can leverage the benefits of symbolic truth maintenance as well as that of probabilistic networks.
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