Combining functions for certainty degrees in consulting systems
International Journal of Man-Machine Studies - Ellis Horwood series in artificial intelligence
Probabilistic reasoning in expert systems: theory and algorithms
Probabilistic reasoning in expert systems: theory and algorithms
Improving accuracy by combining rule-based and case-based reasoning
Artificial Intelligence
An integrated approach of rule-based and case-based reasoning for decision support
CSC '91 Proceedings of the 19th annual conference on Computer Science
Integrating Hybrid Rule-Based with Case-Based Reasoning
ECCBR '02 Proceedings of the 6th European Conference on Advances in Case-Based Reasoning
Rule-based and case-based reasoning approach for internal audit of bank
Knowledge-Based Systems
Reinforcing fuzzy rule-based diagnosis of turbomachines with case-based reasoning
International Journal of Knowledge-based and Intelligent Engineering Systems
Expert Systems with Applications: An International Journal
Combining case-based and rule-based reasoning: a heuristic approach
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
How to combine CBR and RBR for diagnosing multiple medical disorder cases
ICCBR'05 Proceedings of the 6th international conference on Case-Based Reasoning Research and Development
MedCase: a template medical case store for case-based reasoning in medical decision support
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
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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.