Case-based reasoning (CBR) and case-based design (CBD) have established themselves as key mechanisms for approximate reasoning in intelligent and decision support systems. CBR offers a powerful environment for generalizing based on accumulated experiences represented as a finite collection of cases, which embody the existing domain knowledge. When faced with new situations, we invoke and modify these cases to arrive at relevant decisions or design alternatives. The uncertainty or granularity of the resulting decisions is inherently linked to the nature of the cases and the way partial matching occurs between historical findings and current evidence. This book by Professors Avramenko and Kraslawski stands out for its in-depth exploration of CBR and CBD, focusing on the algorithmic aspects of the paradigm. Additionally, the authors present an excellent applied research framework that demonstrates how these concepts can be effectively utilized in the complex environment of process engineering—a topic that is seldom covered in such detail in existing literature.
