Graph Grammar Induction on Structural Data for Visual Programming

Authors:
Keven Ates;Jacek Kukluk;Lawrence Holder;Diane Cook;Kang Zhang
Affiliations:
University of Texas at Dallas, USA;University of Texas at Arlington, USA;University of Texas at Arlington, USA;University of Texas at Arlington, USA;University of Texas at Dallas, USA
Venue:
ICTAI '06 Proceedings of the 18th IEEE International Conference on Tools with Artificial Intelligence
Year:
2006

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Abstract

Computer programs that can be expressed in two or more dimensions are typically called visual programs. The underlying theories of visual programming languages involve graph grammars. As graph grammars are usually constructed manually, construction can be a time-consuming process that demands technical knowledge. Therefore, a technique for automatically constructing graph grammars--at least in part--is desirable. An induction method is given to infer node replacement graph grammars. The method operates on labeled graphs of broad applicability. It is evaluated by its performance on inferring graph grammars from various structural representations. The correctness of an inferred grammar is verified by parsing graphs not present in the training set.