Exploiting Markov Random Fields to Enhance Retrieval in Case-Based Reasoning

The similarity assumption in Case-Based Reasoning (similar problems have similar solutions) has been questioned by several researchers. If knowledge about the adaptability of solutions is available, it can be exploited in order to guide retrieval. Several approaches have been proposed in this context, often assuming a similarity or cost measure defined over the solution space. In this paper, we propose a novel approach where the adaptability of the solutions is captured inside a metric Markov Random Field (MRF). Each case is represented as a node in the MRF, and edges connect cases whose solutions are close in the solution space. States of the nodes represent the adaptability effort with respect to the query. Potentals are defined to enforce connected nodes to share the same state; this models the fact that cases having similar solutions should have the same adaptability effort with respect to the query. The main goal is to enlarge the set of potentially adaptable cases that are retrieved (the recall) without significantly sacrificing the precision of retrieval. We will report on some experiments concerning a retrieval architecture where a simple kNN retrieval is followed by a further retrieval step based on MRF inference.