Neo: Learning Conceptual Knowledge by Sensorimotor Interaction with an Environment

Recent developments in philosophy, linguistics, developmental psychology and arti cial intelligence make it possible to envision a developmental path for an arti cial agent, grounded in activity-based sensorimotor representations. This paper describes how Neo, an arti cial agent, learns concepts by interacting with its simulated environment. Relatively little prior structure is required to learn fairly accurate representations of objects, activities, locations and other aspects of Neo's experience. We show how classes (categories) can acted from these representations, and discuss how our representation might be extended to express physical schemas, general, domain-independent activities that could be the building blocks of concept formation.

• Type: Conference
• Year: 1997
• Where: AGENTS
• Authors: Paul R. Cohen, Marc S. Atkin, Tim Oates, Carole R. Beal
• Keywords: Activity-based Sensorimotor Representations, AGENTS 1997, Arti Cial Agent, Arti Cial Intelligence, Security Privacy