Approaching Adaptation Guided Retrieval in Case-Based Reasoning through
  Inference in Undirected Graphical Models
  In Case-Based Reasoning, when the similarity assumption does not hold, the
retrieval of a set of cases structurally similar to the query does not
guarantee to get a reusable or revisable solution. Knowledge about the
adaptability of solutions has to be exploited, in order to define a method for
adaptation-guided retrieval. We propose a novel approach to address this
problem, where knowledge about the adaptability of the solutions is captured
inside a metric Markov Random Field (MRF). Nodes of the MRF represent cases and
edges connect nodes whose solutions are close in the solution space. States of
the nodes represent different adaptation levels with respect to the potential
query. Metric-based potentials enforce connected nodes to share the same state,
since cases having similar solutions should have the same adaptability level
with respect to the query. The main goal is to enlarge the set of potentially
adaptable cases that are retrieved without significantly sacrificing the
precision and accuracy of retrieval. We will report on some experiments
concerning a retrieval architecture where a simple kNN retrieval (on the
problem description) is followed by a further retrieval step based on MRF
inference.
