Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
An overview of rough set semantics for modal and quantifier logics
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Rough-Fuzzy Hybridization: A New Trend in Decision Making
Rough-Fuzzy Hybridization: A New Trend in Decision Making
Fuzzy Multiple Attribute Decision Making: Methods and Applications
Fuzzy Multiple Attribute Decision Making: Methods and Applications
Current Approaches to Handling Imperfect Information in Data and Knowledge Bases
IEEE Transactions on Knowledge and Data Engineering
Determinación y manejo de la incertidumbre en los Sistemas Basados en Casos
International Joint Conference, 7th Ibero-American Conference, 15th Brazilian Symposium on AI, IBERAMIA-SBIA 2000, Open Discussion Track Proceedings on AI
Massively Parallel Case-Based Reasoning with Probabilistic Similarity Metrics
EWCBR '93 Selected papers from the First European Workshop on Topics in Case-Based Reasoning
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Generally, most Decision Systems do not consider the uncertainty that might be present in knowledge. On many occasions, this leads to proposed solutions that are sometimes inconsistent with the expected results. Case-Based Reasoning is one of the techniques of Artificial Intelligence used in the solution of decision-making problems. Consequently, Case-Based Systems, must consider imperfection in the available knowledge about the world. In this paper, we present a model to make case-based decisions under uncertainty conditions. The model uses Decision Trees and Rough Set Theory to assure an efficient access and an adequate retrieval of cases.