Graph Grammar Induction on Structural Data for Visual Programming

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.

• Type: Conference
• Year: 2006
• Where: ICTAI
• Authors: Keven Ates, Jacek P. Kukluk, Lawrence B. Holder, Diane J. Cook, Kang Zhang
• Keywords: Artificial Intelligence, Graph Grammars, ICTAI 2006, Languages Involve Graph, Replacement Graph Grammars