Computational Models of Solving Raven's Progressive Matrices: A Comprehensive Introduction
Abstract
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.
Cite
@article{arxiv.2302.04238,
title = {Computational Models of Solving Raven's Progressive Matrices: A Comprehensive Introduction},
author = {Yuan Yang and Mathilee Kunda},
journal= {arXiv preprint arXiv:2302.04238},
year = {2023}
}