  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.
