Abstract
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.
Highlights
Rule-based reasoning (RBR) and case-based reasoning (CBR) are two complementary alternatives for building knowledge-based “intelligent” decision-support systems
The central point of all these systems was the compositional approach to inference, allowing us to compose the contributions of multiple rules using a uniform combination function, regardless of their mutual dependencies
0.56 flexibility to the logical formulae used in rule condition and conclusion and introduced integrity constraints allowing detection of inconsistent weight patterns
Summary
Rule-based reasoning (RBR) and case-based reasoning (CBR) are two complementary alternatives for building knowledge-based “intelligent” decision-support systems. One of them is to use machine learning techniques to acquire knowledge from data representing situations successfully solved in the past Another way is to use case-based reasoning, where the knowledge is represented in the form of a “list” of prototype problems and their solutions, so-called cases [5]. CBR systems solve new problems by analogy, that is, by matching and adapting cases that have successfully been solved before This seems to be a more psychologically plausible model of human reasoning than using rules as we do in classical rulebased (expert) systems. The system proposed by Chi and Kiang combines case-based reasoning to solve the problem with a rule-based explanation mechanism. The system is freely available for download at http://sorry.vse.cz/∼berka/NEST/
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