A Logic and Adaptive Approach for Efficient Diagnosis Systems using CBR
  Case Based Reasoning (CBR) is an intelligent way of thinking based on
experience and capitalization of already solved cases (source cases) to find a
solution to a new problem (target case). Retrieval phase consists on
identifying source cases that are similar to the target case. This phase may
lead to erroneous results if the existing knowledge imperfections are not taken
into account. This work presents a novel solution based on Fuzzy logic
techniques and adaptation measures which aggregate weighted similarities to
improve the retrieval results. To confirm the efficiency of our solution, we
have applied it to the industrial diagnosis domain. The obtained results are
more efficient results than those obtained by applying typical measures.
