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Abstract
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.
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Authors and Affiliations
Department of Computer Science, Universidad Central de Las Villas, Carretera a Camajuaní km 5.5, Santa Clara, Cuba
Iliana Gutiérrez Martínez & Rafael E. Bello Pérez
- Iliana Gutiérrez Martínez
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- Rafael E. Bello Pérez
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Editors and Affiliations
Departament d’Enginyeria i Ciència dels Computadors, Universitat Jaume 1, Campus de Riu Sec, 12071, Castellón, Spain
M. Teresa Escrig & Francisco Toledo &
Computer Science Department, Universitat Ramon Llull, Passeig Bonanova, 8, 08022, Barcelona, Catalunya, Spain
Elisabet Golobardes
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Martínez, I.G., Pérez, R.E.B. (2002). A Decision Case-Based System, That Reasons in Uncertainty Conditions. In: Escrig, M.T., Toledo, F., Golobardes, E. (eds) Topics in Artificial Intelligence. CCIA 2002. Lecture Notes in Computer Science(), vol 2504. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36079-4_5
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