Abstract
Intelligent systems are technologically advanced machines that perceive and respond to the world around them. They can take many forms: facial recognition programs, personalized shopping suggestions, healthcare tools, etc. Research in intelligent systems faces numerous challenges, many of which relate to automatic reasoning. Intelligent systems’ knowledge bases are founded on facts and rules. Rules updates are essential to ensure that the system adapts to its environment evolution. In this paper, we aim to facilitate the automation of rule bases management by eliminating redundancies and handling circularity. This research work is part of the proposition of an approach for automating the management of rule bases. Our method is based on dependency relationships that may exist between the rules. The experimentation results show that our proposition succeeded in eliminating redundancies and detecting a great number of cycles.
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- 1.
The closure of a rule base R: Closure(R)=\(\lbrace R \cup \lbrace all~the~rules~implied~by~R \rbrace \rbrace \).
- 2.
Example 6:R05 and R06 are two rules from the same group Gi.
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Boujelben, A., Amous, I. (2020). Refining Rule Bases for Intelligent Systems: Managing Redundancy and Circularity. In: Abramowicz, W., Klein, G. (eds) Business Information Systems. BIS 2020. Lecture Notes in Business Information Processing, vol 389. Springer, Cham. https://doi.org/10.1007/978-3-030-53337-3_31
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