1995/10/01 by DAVID B. LEAKE, David Leake
Computer Science · #AI-based Problem Solving and Planning #Bayesian Modeling and Causal Inference #Topic Modeling
paper · doi:10.1080/09528139508953820
openalex publication_date 1995/10/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/30
Many abductive understanding systems generate explanations by a backwards chaining process that is neutral both to the explainer's previous experience in similar situations and to why the explainer is attempting to explain. This article examines the relationship of such models to an approach that uses case-based reasoning to generate explanations. In this case-based model, the generation of abductive explanations is focused by prior experience and by goal-based criteria reflecting current information needs. The article analyses the commitments and contributions of this case-based model as applied to the task of building good explanations of anomalous events in everyday understanding. The article identifies six central issues for abductive explanation, compares how these issues are addressed in traditional and case-based explanation models, and discusses benefits of the case-based approach for facilitating generation of plausible and useful explanations in domains that are complex and imperfectly understood.