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Advances in Artificial Intelligence
Volume 2011 (2011), Article ID 374250, 15 pages
Research Article

NEST: A Compositional Approach to Rule-Based and Case-Based Reasoning

1Department of Information and Knowledge Engineering, University of Economics, W. Churchill Square 4, 130 67 Prague 3, Czech Republic
2Centre of Biomedical Informatics, Institute of Computer Science of the Academy of Sciences, Pod Vodarenskou vezi 2, 182 07 Prague 8, Czech Republic

Received 10 December 2010; Revised 9 April 2011; Accepted 16 May 2011

Academic Editor: Weiru Liu

Copyright © 2011 Petr Berka. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.