A neural-dynamic architecture for behavioral organization of an embodied agent

Abstract

How agents generate meaningful sequences of actions in natural environments is one of the most challenging problems in studies of natural cognition and in the design of artificial cognitive systems. Each action in a sequence must contribute to the behavioral objective, while at the same time satisfying constraints that arise from the environment, the agent's embodiment, and the agent's behavioral history. In this paper, we introduce a neural-dynamic architecture that enables selection of an appropriate action for a given task in a particular environment and is open to learning. We use the same framework of neural dynamics for all processes from perception, to representation and motor planning as well as behavioral organization. This facilitates integration and flexibility. The neural dynamic representations of particular behaviors emerge on the fly from the interplay between task and environment inputs as well as behavioral history. All behavioral states are attractors of the neural dynamics, whose instabilities lead to behavioral switches. As a result, behavioral organization is robust in the face of noisy and unreliable sensory information.