Generalized fuzzy automata with semantic computing - Wei, Lina; Huang, Guangjian; Wasti, Shahbaz Hassan; Hussain, Muhammad Jawad; Jiang, Yuncheng (Soft computing (Berlin, Germany), 2021)
Researches about semantic computing are usually evaluated by experiments, lacking support from the theory of computation. But traditional finite automata are defined on a fixed finite set of states. They will be invalid when a different application needs to change the states. However, semantic computing always asks for more robust automata that can modify itself according to the changes of states. Therefore, in this paper, we redefine traditional automata in more robust forms by semantic computing. The intuitive idea is when the state which an automaton is supposed to enter has changed, we drive automata into a state that is similar to the original one. We can find another similar state in the original state set to replace the changed state by semantic similarity. Based on the theories of semantic similarity, there always exists a similar state. Therefore, once a state has changed, we can always find a similar state to replace it, which means that we can empower automata to adapt to the changes of states. Our new automata bridge the gap between semantic computing and the theory of computation. Furthermore, we also redefine fuzzy finite automata and pushdown automata in more robust forms. Finally, we provide an application about the weather forecast, which indicates how to overcome the limitations of traditional automata.