  This paper presents the Never Ending Open Learning Adaptive Framework
(NEOLAF), an integrated neural-symbolic cognitive architecture that models and
constructs intelligent agents. The NEOLAF framework is a superior approach to
constructing intelligent agents than both the pure connectionist and pure
symbolic approaches due to its explainability, incremental learning,
efficiency, collaborative and distributed learning, human-in-the-loop
enablement, and self-improvement. The paper further presents a compelling
experiment where a NEOLAF agent, built as a problem-solving agent, is fed with
complex math problems from the open-source MATH dataset. The results
demonstrate NEOLAF's superior learning capability and its potential to
revolutionize the field of cognitive architectures and self-improving adaptive
instructional systems.
