Abstract
In certain domains a case base may contain contradictory but correct cases. The contradictory solutions are due to known domain and problem characteristics which are not part of the case description, and which cannot be formally or explicitly described. In such situations it is important to develop methods that will use these criteria to select among the competing solutions of the matching cases Our domain of application was the assignment of billing numbers to the shipment of goods, and the case base contained numerous cases of similar or even identical problems that had different solutions (billing numbers). Such contradictory solutions were correct and an outcome of domain constraints and characteristics that were not part of the cases and were also not formally known and defined. It was assumed that the frequency with which a solution appeared among the retrieved cases and the recency of the time the solution had been applied were important for selecting among competing solutions, but there was no explicit way for doing so. In this paper we show how we used genetic algorithms to discover methods to combine and operationalize vague selection criteria such as “recency” and “frequency.” GAs helped us discover selection criteria for the contradictory solutions retrieved by CBR retrieval and significantly improved the accuracy and performance of the CBR system.
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Tsatsoulis, C., Stephens, B. (2003). Using Genetic Algorithms to Discover Selection Criteria for Contradictory Solutions Retrieved by CBR. In: Ashley, K.D., Bridge, D.G. (eds) Case-Based Reasoning Research and Development. ICCBR 2003. Lecture Notes in Computer Science(), vol 2689. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45006-8_43
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DOI: https://doi.org/10.1007/3-540-45006-8_43
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