AI research on metareasoning for agent self-adaptation has generally focused on modifying the agent’s reasoning processes (e.g., ). In this paper, we describe our ongoing work on the equally important problem of using metareasoning for modifying the agent’s domain knowledge. Since classification is a ubiquitous task in AI, we consider the problem of using meta-knowledge for repairing classification knowledge when the classifier supplies an incorrect class label. More specifically, we consider the subclass of classification problems that can be decomposed into a hierarchical set of smaller classification problems; alternatively, problems in which features describing the world are progressively aggregated and abstracted into higher-level abstractions until a class label is produced at the root node. This subclass of classification problems is recognized as capturing a common pattern of classification  . We will call this classification task compositional classification, and the hierarchy of abstractions an abstraction network (AN).