Probability logic reasoning with threshold-surety based on fuzzy c-partition entropy approach

We propose a method of probability logic reasoning with threshold-surety to the problem of making inference from a knowledge base in experts system, which treats a finite set of premises in the knowledge base as assertions that are true with probability at least some primary threshold, and consider as a finite set of alternative conclusions those assertions probabilistically entailed to have probability at least some suitable threshold. Based on comparative probability theory, we construct a probability entailment relation between premises and conclusions, and use the method of fuzzy c-partition and the maximum fuzzy entropy principle to select the threshold values. An illustrative example of application of our method shows how we make inferences from knowledge base.