Producing an artificial general intelligence (AGI) has been an elusive goal
in artificial intelligence (AI) research for some time. An AGI would have the
capability, like a human, to be exposed to a new problem domain, learn about it
and then use reasoning processes to make decisions. While AI techniques have
been used across a wide variety of problem domains, an AGI would require an AI
that could reason beyond its programming and training. This paper presents a
small step towards producing an AGI. It describes a mechanism for an AI to
learn about and develop reasoning pathways to make decisions in an a priori
unknown domain. It combines a classical AI technique, the expert system, with a
its modern adaptation - the gradient descent trained expert system (GDTES) -
and utilizes generative artificial intelligence (GAI) to create a network and
training data set for this system. These can be created from available sources
or may draw upon knowledge incorporated in a GAI's own pre-trained model. The
learning process in GDTES is used to optimize the AI's decision-making. While
this approach does not meet the standards that many have defined for an AGI, it
provides a somewhat similar capability, albeit one which requires a learning
process before use.

通过结合经典人工智能技术 —— 专家系统与现代适应性梯度下降训练的专家系统（GDTES），利用生成式人工智能（GAI）创建网络和训练数据集的机制，本研究提出了一种人工智能的学习和发展推理路径以在先验未知领域做决策的方法，并展示了朝着产生人工通用智能（AGI）的目标迈出的小步骤。