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

## TL;DR

This paper introduces a novel adaptation-guided retrieval method in case-based reasoning using Markov Random Fields to improve retrieval of adaptable cases, especially when similarity assumptions are weak.

## Contribution

It proposes a new approach that models case adaptability within an MRF, enhancing retrieval by considering solution space proximity and adaptability levels.

## Key findings

- Enlarges the set of potentially adaptable cases without losing accuracy
- Uses MRF inference to improve retrieval in case-based reasoning
- Combines simple kNN retrieval with MRF-based refinement

## Abstract

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.