19th AIAI 2023, 14 - 17 June 2023, León, Spain

Exploring the power of failed experiences in case-based reasoning for Improved Decision Making

Fateh Boulmaiz, Patrick Regnier, Stephane Ploix


  Case-based reasoning (CBR) is a popular approach for problem-solving and decision-making that involves using previous cases as a basis for reasoning about new situations. While CBR has shown promise in many domains, it is not immune to errors and failures. One limitation of the approach is that it tends to focus primarily on successful cases, ignoring the potential value of failed cases as a source of learning and insight. While many studies have focused on the role of successful cases in CBR, less attention has been given to the value of analyzing failed cases. In this paper, we explore the benefits of reasoning from both successful and failed cases in CBR. We argue that by examining both types of cases, we can identify patterns and insights that can help to refine CBR methods, improve their accuracy and efficiency, and reduce the likelihood of future failures. Using a combination of theoretical modeling and empirical analysis, we demonstrate that failed cases can provide valuable insights into identifying potential solutions that might otherwise be overlooked. To illustrate our approach, we present a case study in which we apply our reasoning methodology to a real-world problem in the field of energy management. Our analysis demonstrates that by considering both successful and failed cases, we can identify new and more effective solutions to the problem at hand.  

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