  The creation of machine learning algorithms for intelligent agents capable of
continuous, lifelong learning is a critical objective for algorithms being
deployed on real-life systems in dynamic environments. Here we present an
algorithm inspired by neuromodulatory mechanisms in the human brain that
integrates and expands upon Stephen Grossberg\'s ground-breaking Adaptive
Resonance Theory proposals. Specifically, it builds on the concept of
uncertainty, and employs a series of neuromodulatory mechanisms to enable
continuous learning, including self-supervised and one-shot learning. Algorithm
components were evaluated in a series of benchmark experiments that demonstrate
stable learning without catastrophic forgetting. We also demonstrate the
critical role of developing these systems in a closed-loop manner where the
environment and the agent\'s behaviors constrain and guide the learning
process. To this end, we integrated the algorithm into an embodied simulated
drone agent. The experiments show that the algorithm is capable of continuous
learning of new tasks and under changed conditions with high classification
accuracy (greater than 94 percent) in a virtual environment, without
catastrophic forgetting. The algorithm accepts high dimensional inputs from any
state-of-the-art detection and feature extraction algorithms, making it a
flexible addition to existing systems. We also describe future development
efforts focused on imbuing the algorithm with mechanisms to seek out new
knowledge as well as employ a broader range of neuromodulatory processes.
