GRAMMATICAL INFERENCE FOR THE AUTOMATIC GENERATION OF VISUAL LANGUAGES

Abstract

In this paper we address the problem of the automatic generation of visual languages from a sample set of visual sentences. We present an improvement of the inference module of the VLG system which was originally conceived for the generation of iconic languages [11]. With this extension any kind of visual languages, like diagrams and forms, can be considered. To this aim, we present an inference algorithm for the class of Boundary SR grammars. These grammars are a subclass of the SR grammars with the interesting property of confluence, which extends the concept of context-freeness to the case of nonlinear grammars. Moreover, in spite of the simplicity and naturalness of the formalism, the generative power of this class is sufficient to specify interesting visual languages. The inference algorithm exploits an elegant characterization of Boundary SR languages in terms of tree and string languages. More precisely, we show that a visual language is a Boundary SR language if and only if it can be defined as a regular tree language and a set of properly associated string languages. Based on this result, the problem of identifying structural properties in a diagrammatic visual sentence is brought back to the detection of structural properties in tree and string languages. The main advantage coming from the use of a grammatical inference technique in visual language specification is that the designer only needs to specify a set of visual sentences that he/she feels to sufficiently exemplify the intended target language.