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In Case-Based Reasoning, when the similarity assumption does not hold, theretrieval of a set of cases structurally similar to the query does notguarantee to get a reusable or revisable solution. Knowledge about theadaptability of solutions has to be exploited, in order to define a method foradaptation-guided retrieval. We propose a novel approach to address thisproblem, where knowledge about the adaptability of the solutions is capturedinside a metric Markov Random Field (MRF). Nodes of the MRF represent cases andedges connect nodes whose solutions are close in the solution space. States ofthe nodes represent different adaptation levels with respect to the potentialquery. Metric-based potentials enforce connected nodes to share the same state,since cases having similar solutions should have the same adaptability levelwith respect to the query. The main goal is to enlarge the set of potentiallyadaptable cases that are retrieved without significantly sacrificing theprecision and accuracy of retrieval. We will report on some experimentsconcerning a retrieval architecture where a simple kNN retrieval (on theproblem description) is followed by a further retrieval step based on MRFinference.

Approaching Adaptation Guided Retrieval in Case-Based Reasoning through Inference in Undirected Graphical Models