Intelligence through interaction: towards a unified theory for learning / A.-H.Tan

Abstract. Machine learning, a cornerstone of intelligent systems, has typically been studied in the context of specific tasks, including clus-tering (unsupervised learning), classification (supervised learning), and control (reinforcement learning). This paper presents a learning archi-tecture within which a universal adaptation mechanism unifies a rich set of traditionally distinct learning paradigms, including learning by matching, learning by association, learning by instruction, and learning by reinforcement. In accordance with the notion of embodied intelli-gence, such a learning theory provides a computational account of how an autonomous agent may acquire the knowledge of its environment in a real-time, incremental, and continuous manner. Through a case study on a minefield navigation domain, we illustrate the efficacy of the proposed model, the learning paradigms encompassed, and the various types of knowledge learned.