Model Generation Of Truth Level Of Formula In Operator Fuzzy Logic

Logical reasoning is one of the most important areas of the artificial intelligence. With the development of artificial intelligence, classical logic has great limitations to describe and process the deduction based on knowledge. So there is a pressing need for a more generalized reasoning system. Liu Xuhua put forward operator fuzzy logic in 1985,and based on operator fuzzy logic Deng Ansheng put forward bool operator fuzzy logic in 1994. In operator fuzzy logic, the value range of variables is real number in [0,1], and operator fuzzy logic defines a real numberλ∈[0,1] as an operator, e.g.λA is presented " A satisfies at the possibility of A". It is more objective to describe knowledge with operator fuzzy logic.The main work of the paper is to solve model generation of truth level and false level for any given wffs in{0,1} and [0,1] within the frame work of operator fuzzy logic. This paper falls into two parts - theory and system. Theoretical part is mainly based on the thinking of quantitative model. When the value of variables of wffs is 0 and 1, according to theoremλρ=λ(?)ρ, we can get the model generation of truth level and false level easily by separating operators and variables. When the value range of variables is in [0,1], we consider the fuzzy variables like the formλρas a unit. According to theorem min{λ,1-λ}≤T1(λρ)≤max{λ,1-λ},we can find the extreme point of T1(λρ). Combining with operational character of OFL, we can get the model generation of truth level and false level. System part provides a simple and convenient interface. When the user input a wff, the system automatically calls the algorithm of the model generation of truth level and false level, and outputs the results.For any wffs in operator fuzzy logic, we can know how credible the formula is by solving truth level and false level. Solving the truth level and false level model of the wffs, not only has obvious theoretical significance, but also may provide the guide for deductive reasoning in semantics by using the model.