During the study of incidence calculus theory, we firstly clarified original incidence calculus and the probabilistic reasoning mechanism on it. We then introduced the improvement of generalized incidence calculus theory (GICT) proposed by Liu~, which establishes incidence calculus theory on Lukasiewicz's three valued logic.
Basing on the theory discussed above, we analysed the probabilistic reasoning of IICT, and then proposed the relationship between the two interval incidence calculus theories, and the probabilistic reasoning mechanism according to the probabilistic reasoning of IICT.
Then the feature of AntNet algorithm is primary analyzed and two shortages in AntNet is presented: the first, AntNet only present the probabilistic reasoning feature in computational intelligence, the complementary feature is not presented.
The concept of soft computing and its three basic constituents: fuzzy logic, neural networks and probabilistic reasoning are introduced. The article then discusses a new style fuzzy controller based on soft computing and its application.
The approach presnted in this paper can satisfy the exact probability reasoning; contain the uncertainty of the information, effectively avoid the unknown for the relationship among the rules, and the computing complexity of its briefest inference is linear to the number of inferential nodes.
The authors firstly outline BBN and probability reasoning, then propose and implement our intelligent tutoring system that uses BBN as student models, and emphasize the structure and functions and probability reasoning algorithm of student models.
After discussing the theory foundations of the certainty factor model and its drawbacks, the equivalence of the probabilistic inference equation of a Noisy-OR model, a simplified BN model, and the inference equation of a certainty factor model is argued. The advantages of BNs over the certainty factor models are discussed in knowledge representation and acquisition and their inference.