Automating visual language generation

A system to generate and interpret customized visual languages in given application areas is presented. The generation is highly automated. The user presents a set of sample visual sentences to the generator. The generator uses inference grammar techniques to produce a grammar that generalizes the initial set of sample sentences, and exploits general semantic information about the application area to determine the meaning of the visual sentences in the inferred language. The interpreter is modeled on an attribute grammar. A knowledge base, constructed during the generation of the system, is then consulted to construct the meaning of the visual sentence. The architecture of the system and its use in the application environment of visual text editing (inspired by the Heidelberg icon set) enhanced with file management features are reported