Rule-based reasoning (RBR) and case-based reasoning (CBR) are two complementary alternatives for building knowledge-based “intelligent” decision-support systems. RBR and CBR can be combined in three main ways: RBR first, CBR first, or some interleaving of the two. The NEST system, described in this paper, allows us to invoke both components separately and in arbitrary order. In addition to the traditional network of propositions and compositional rules, NEST also supports binary, nominal, and numeric attributes used for derivation of proposition weights, logical (no uncertainty) and default (no antecedent) rules, context expressions, integrity constraints, and cases. The inference mechanism allows use of both rule-based and case-based reasoning. Uncertainty processing (based on Hájek's algebraic theory) allows interval weights to be interpreted as a union of hypothetical cases, and a novel set of combination functions inspired by neural networks has been added. The system is implemented in two versions: stand-alone and web-based client server. A user-friendly editor covering all mentioned features is included. 1. Introduction Rule-based reasoning (RBR) and case-based reasoning (CBR) are two complementary alternatives for building knowledge-based “intelligent” decision-support systems. The first approach is closely related to expert systems. Expert Systems (ES) are typically defined as computer programs that emulate the decision-making ability of a human expert. The power of an ES is derived from the presence of a knowledge base filled with expert knowledge, mostly in symbolic form. In addition, there is a generic problem-solving mechanism used as the inference engine [1]. Some other typical features of expert systems include uncertainty processing, dialogue mode of the consultation, and explanation abilities. Beside ES dedicated to specific applications, “empty” expert systems (also called “shells”) have been developed, which can be coupled with an arbitrary knowledge base encoded in an appropriate format. Research in the area of expert systems started in the mid-1970s, classical examples of early systems that influenced other researchers are MYCIN [2] and PROSPECTOR [3]. The knowledge of an expert is usually represented in the form of IF-THEN rules, which are applied in a deductive way: if the condition of a rule is satisfied, then this rule can be applied to either derive some conclusion or to perform the respective actions. The central point of all these systems was the compositional approach to inference, allowing us to compose the contributions of
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