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.

翻译：本文提出了永续开放学习自适应框架（NEOLAF），这是一种集成的神经符号认知架构，用于建模和构建智能体。相较于纯连接主义和纯符号方法，NEOLAF框架在可解释性、增量学习、效率、协作与分布式学习、人机协同以及自我改进方面具有显著优势，从而成为构建智能体的更优路径。本文进一步展示了一项令人信服的实验：将构建为问题求解智能体的NEOLAF代理，输入来自开源MATH数据集的复杂数学问题。实验结果表明，NEOLAF具备卓越的学习能力，并有望彻底改变认知架构与自适应改进教学系统的领域。