SUBSTITUTIONAL ADAPTATION IN CASE-BASED REASONING: A GENERAL FRAMEWORK APPLIED TO P-TRUCK CURING
Sara Manzoni, Fábio Sartori, Giuseppe Vizzari · Applied Artificial Intelligence · 2007
Adaptation is one of the most problematic steps in the design and development of case-based reasoning (CBR) systems. In fact, it may require considerable domain knowledge and involve complex knowledge engineering tasks, whereas CBR is often adopted when available domain knowledge is not enough to build a problem solution given its description, and thus past experiences are considered and exploited. This paper introduces a general framework for substitutional adaptation, which only requires analogical domain knowledge, which is very similar to the one required to define a similarity function. The approach is formally introduced, and its applicability is discussed with reference to case structure and its variability. A case study focused on the adaptation of cases related to truck tire production processes is also presented.