Automating Visual Language Generation.

The most important feature of visual languages is their ability to represent a user's mental concepts pictorially, thus providing for ease of learning and use. In order for this feature to be best exploited, there must be a close connection between the user's mental model of the application environment and the meaning of the icons in the system. Thus, a visual language must be accurately tuned to the application environment to which it is directed. This paper presents a system to generate and interpret customized visual languages in given application areas. 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 whole system and its use in the application environment of visual text editing (inspired by the Heidelberg Icon Set) enhanced with file management features is reported. [ABSTRACT FROM AUTHOR]