Feature Weighting by Explaining Case-Based Problem Solving Episodes
Héctor Muñoz‐Avila, Jochem Huellen · 1996
We present a similarity criterion based on feature weighting. Feature weights are recomputed dynamically according to the performance of cases during problem solving episodes. We will also present a novel algorithm to analyze and explain the performance of the retrieved cases and to determine the features whose weights need to be recomputed. We will perform experiments and show that the integration in a feature weighting model of our similarity criterion with our analysis algorithm improves the adaptability of the retrieved cases by converging to best weights for the features over a period of multiple problem solving episodes. 1 Introduction An essential factor influencing the effectiveness of case-based problem solving is the retrieval phase (Aamodt and Plaza, 1994). In the context of planning and design, retrieval means searching for adaptable cases (Smyth and Keane, 1994; Marir and Watson, 1995; Smyth and Keane, 1995). Thus, any similarity criterion should measure the adaptation ef...