Computational Models of Solving Raven's Progressive Matrices: A Comprehensive Introduction
It offers a review for researchers interested in intelligence testing and AI problem-solving, but is incremental as it synthesizes existing work without new results.
This paper provides a comprehensive introduction to computational models for solving Raven's Progressive Matrices (RPM), covering the history, theories, item design, and evolution of these models, with suggestions for linking human and AI intelligence testing.
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.