An Epistemological Comparison between Fuzzy Logic Engines and Bayesian Filters

In this paper we analyze two methods of artificial intelligence: the Bayesian filter and the Fuzzy Logic engine. In order to do this we present each method and compare them. The mentioned methods have similar backgrounds but from epistemological point of view they are different. The paper ends with three case studies: the first about a Fuzzy Logic engine which is integrated into a Bayesian filter, this give us the possibility to underline the mentioned difference; the second case study about a mobile robot, where we present the main advantage of the Bayesian filter, which is the possibility to compute the degree of true about the model result; and the third case study about human decision modeling with Bayesian reasoning, where we underline the flexibility of the method . Key-Words: Artificial intelligence methods, Fuzzy Logic, Plausible reasoning, Bayesian filter, Degree of true