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Semantics-Based Inference Algorithms for Adaptive Visual Environments
October 1996 (vol. 22 no. 10)
pp. 730-750

Abstract—The paper presents a grammatical inference methodology for the generation of visual languages, that benefits from the availability of semantic information about the sample sentences. Several well-known syntactic inference algorithms are shown to obey a general inference scheme, that we call Gen-Inf scheme. Then, all the algorithms of the Gen-Inf scheme are modified in agreement with the introduced semantics-based inference methodology.

The use of grammatical inference techniques in the design of adaptive user interfaces was previously experimented with the VLG system for visual language generation. The system is a powerful tool for specifying, designing, and interpreting customized visual languages for different applications. In the paper we enhance the adaptivity of the VLG system to any visual environment by exploiting the proposed semantics-based inference methodology. As a matter of fact, a more general model of visual language generation is achieved, based on the Gen-Inf scheme, where the end-user is allowed to choose the algorithm which best fits his/her requirements within the particular application environment.

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Index Terms:
Grammatical inference, adaptive user interfaces, visual language design, semantic similarity.
Citation:
Filomena Ferrucci, Genoveffa Tortora, Maurizio Tucci, Giuliana Vitiello, "Semantics-Based Inference Algorithms for Adaptive Visual Environments," IEEE Transactions on Software Engineering, vol. 22, no. 10, pp. 730-750, Oct. 1996, doi:10.1109/32.544351
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