Hybrid model of general fuzzy automata and semantic computing: an application to transportation e-service

The computing models such as crisp automata, fuzzy automata and general fuzzy automata (GFA) are used to represent complex systems for predefined input alphabets or symbols. A framework that can process words rather than symbols is needed to simulate applications based on the natural language. Semantic computing (SC) offers a technique to accommodate semantically similar words instead of predefined words, thus extends the applicability and flexibility of GFA. In present work, a hybrid model of GFA and SC is proposed to deal with a situation where input can be user-dependent or related to words that have semantically similar meanings. In traditional theory of automata, if input symbols are changed one must define a new automata, whereas in the proposed work instead of defining a new GFA, existing GFA can process the semantically similar external words. An application related to transportation e-service is further discussed to understand the enhanced flexibility and applicability of the proposed models.