Metareasoning-Based Learning for Classification Hierarchies

This paper takes a metareasoning-based approach to classification learning, framing the learning problem as one of self-diagnosis and self-adaptation. Artificial Intelligence (AI) research on metareasoning for agent self-adaptation has generally focused on modifying the agent’s reasoning processes. In this paper, we describe the use of metareasoning for retrospective adaptation of the agent’s domain knowledge. In particular, we consider the use of meta-knowledge for structural credit assignment in a classification hierarchy when the classifier makes an incorrect prediction. We present a scheme in which the semantics of the intermediate abstractions in the classification hierarchy are grounded in percepts in the world, and show that this scheme enables self-diagnosis and self-repair of knowledge contents at intermediate nodes in the hierarchy. We also provide an empirical evaluation of the technique.