Intelligence Through Interaction: Towards a Unified Theory for Learning [chapter]

Ah-Hwee Tan, Gail A. Carpenter, Stephen Grossberg
2007 Lecture Notes in Computer Science  
Machine learning, a cornerstone of intelligent systems, has typically been studied in the context of specific tasks, including clustering (unsupervised learning), classification (supervised learning), and control (reinforcement learning). This paper presents a learning architecture 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
more » ... ement. In accordance with the notion of embodied intelligence, 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.
doi:10.1007/978-3-540-72383-7_128 fatcat:2qwsva5ujvfahlzwntn4mfer2u