  Automatic programming has been actively studied for a long time by various
approaches including genetic programming. In recent years, automatic
programming using neural networks such as GPT-3 has been actively studied and
is attracting a lot of attention. However, these methods are illogical
inference based on experience by enormous learning, and their thinking process
is unclear. Even using the method by logical inference with a clear thinking
process, the system that automatically generates any programs has not yet been
realized. Especially, the inductive inference generalized by logical inference
from one example is an important issue that the artificial intelligence can
acquire knowledge by itself. In this study, we propose NP4G: Network
Programming for Generalization, which can automatically generate programs by
inductive inference. Because the proposed method can realize "sequence",
"selection", and "iteration" in programming and can satisfy the conditions of
the structured program theorem, it is expected that NP4G is a method
automatically acquire any programs by inductive inference. As an example, we
automatically construct a bitwise NOT operation program from several training
data by generalization using NP4G. Although NP4G only randomly selects and
connects nodes, by adjusting the number of nodes and the number of phase of
"Phased Learning", we show the bitwise NOT operation programs are acquired in a
comparatively short time and at a rate of about 7 in 10 running. The source
code of NP4G is available on GitHub as a public repository.
