  As being widely used to measure human intelligence, Raven's Progressive
Matrices (RPM) tests also pose a great challenge for AI systems. There is a
long line of computational models for solving RPM, starting from 1960s, either
to understand the involved cognitive processes or solely for problem-solving
purposes. Due to the dramatic paradigm shifts in AI researches, especially the
advent of deep learning models in the last decade, the computational studies on
RPM have also changed a lot. Therefore, now is a good time to look back at this
long line of research. As the title -- ``a comprehensive introduction'' --
indicates, this paper provides an all-in-one presentation of computational
models for solving RPM, including the history of RPM, intelligence testing
theories behind RPM, item design and automatic item generation of RPM-like
tasks, a conceptual chronicle of computational models for solving RPM, which
reveals the philosophy behind the technology evolution of these models, and
suggestions for transferring human intelligence testing and AI testing.
