A Unifying Computational Framework for Teaching and Active Learning

Abstract

Traditionally, learning has been modeled as passively obtaining information or actively exploring the environment. Recent research has introduced models of learning from teachers that involve reasoning about why they have selected particular evidence. We introduce a computational framework that takes a critical step toward unifying active learning and teaching by recognizing that meta-reasoning underlying reasoning about others can be applied to reasoning about oneself. The resulting Self-Teaching model captures much of the behavior of information-gain-based active learning with elements of hypothesis-testing-based active learning and can thus be considered as a formalization of active learning within the broader teaching framework. We present simulation experiments that characterize the behavior of the model within three simple and well-investigated learning problems. We conclude by discussing such theory-of-mind-based learning in the context of core cognition and cognitive development.