It is well known that both major directions of AI research Neural Networks and Expert Systems exhibit their strengths and weaknesses in almost complementary way. While neural networks are not good in higher-level reasoning (mainly for lack of proper representation as well as proper training methods), they are very good in imprecise classification and recognition. Expert systems on the other hand are reasonably good in higher-level reasoning, but they are fundamentally weak in handling imprecise and uncertain knowledge and data. This weakness of expert systems in handling uncertainty has been addressed by many researchers and many methods have been proposed to deal with it. We have also contributed towards the solution of this problem by designing and building a software tool McESE to help create expert systems. We had had several objectives on our mind while designing the system, but one of them was to give the knowledge engineer a possibility to deal with uncertainty in different and more flexible ways and not to be &quot;locked &quot; in any single approach. McESE knowledge base consists of a set of rules in the for
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