On the semantics of fuzzy logic
International Journal of Approximate Reasoning
Gradual inference rules in approximate reasoning
Information Sciences: an International Journal
Equality relations as a basis for fuzzy control
Fuzzy Sets and Systems
Integration of Learning into a Knowledge Modelling Framework
EKAW '94 Proceedings of the 8th European Knowledge Acquisition Workshop on A Future for Knowledge Acquisition
Similarity-based Consequence Relations
ECSQARU '95 Proceedings of the European Conference on Symbolic and Quantitative Approaches to Reasoning and Uncertainty
Integrating Induction in a Case-Based Reasoner
EWCBR '94 Selected papers from the Second European Workshop on Advances in Case-Based Reasoning
A Method for Predicting Solutions in Case-Based Problem Solving
EWCBR '00 Proceedings of the 5th European Workshop on Advances in Case-Based Reasoning
Flexible Control of Case-Based Prediction in the Framework of Possibility Theory
EWCBR '00 Proceedings of the 5th European Workshop on Advances in Case-Based Reasoning
Collaborative Filtering Methods for Binary Market Basket Data Analysis
AMT '01 Proceedings of the 6th International Computer Science Conference on Active Media Technology
Exploiting Similarity for Supporting Data Analysis and Problem Solving
IDA '99 Proceedings of the Third International Symposium on Advances in Intelligent Data Analysis
On learning similarity relations in fuzzy case-based reasoning
Transactions on Rough Sets II
Fundamenta Informaticae
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This article approaches the formalization of inference in Case-based Reasoning (CBR) systems. CBR systems infer solutions of new problems on the basis of a precedent case that is, to some extent, similar to the current problem. Using the logics developed for similarity-based inference we characterize CBR systems defining what we call the Precedent-based Plausible Reasoning (PPR) model. This model is based on the graded consequence relations named approximation entailment and proximity entailment. A modal interpretation is provided for the precedent-based inference where the plausibility is given by the graded possibility operator @?"@g. The PPR model shows that both knowledge-intensive CBR systems and the nearest neighbor algorithms share a common core formalism and that their difference is on whether or not (respectively) they use a general theory in addition to the precedent cases.