# Raven’s Progressive Matrices – Create an Intelligent AI Test Solver (6 methods)

## Project Goals

One goal of knowledge-based AI is to create human-like, human-level intelligence. In this post, you will find the steps and methods that you can use to solve Raven’s Progressive Matrices with AI.

I developed this project during my Knowledge-Based AI course at Georgia Tech. While I cannot go into too many specifics around how I implemented this AI agent, I will go over the high-level concepts and algorithms that anyone can use to develop the same type of agent.

## Solving Raven’s Progressive Matrices

Raven’s Progressive Matrices (often referred to simply as Raven’s Matrices) or RPM is a nonverbal test used in measuring abstract reasoning and regarded as a non-verbal estimate of fluid intelligence. It is a textbook example of an intelligence test and is displayed as the first image on the Wikipedia page for IQ.

## The Problem Statement

The problem statement for this project can be distilled down to this: Given a two-by-two (shown above), or three by three RPM image, and six or eight answers (respectively), choose the correct answer that logically follows the 2×2 or 3×3 pattern. For example, the answer to the above question is #5, as the inner diamond disappeared from A to B, and it logically follows that the inner diamond would also disappear in the C to D transformation.

## The Algorithms

Here is the set of algorithms that I tested to try and visually solve the Raven’s Progressive Matrices problems. Most of these can be distilled down into algorithms that, in their own ways, attempt to find similarities or dissimilarities between images and then correlate those results with possible answer pairs.

### Affine & Fractal Methods

The Affine algorithm it is somewhat simplistic and can be built upon to make the fractal method. However, it is mostly suited for solving smaller matrices (2×2).

The Affine method is a generate & test-based algorithm that assumes relationships between the columns and rows in an RPM problem and performs a set of similitude transformations (e.g. mirroring, flipping, or rotating the image) on the known elements (Kunda, McGreggor, and Goel, 2010).

The known elements in a 2×2 matrix are A, B, and C and transformations will be applied to row A->B and column A->C. The Affine method will perform a set of defined transformations and will select the one that produces the most similar output; for example, if rows A and B are mirrored, the Affine algorithm may (correctly) think that the ’Mirror’ transformation was applied on row A->B.

This chosen transformation will then be applied to the other row or column. For the example above, the ’Mirror’ transformation will be applied on C->? and a generated image will be made. This generated image will then be compared against the six possible answers and the answer that is most similar to the generated image will be chosen.

The Affine method also seeks to represent extra nuance in the transformations in the form of addition and subtraction of data in the generated compositions. This representation of image composition in addition to the similitude image transformations described above should allow the Affine algorithm to be fairly accurate while remaining fairly simple.

Here is an example of my generate and test method’s output: it received, A, B, and C as input, found that if it rotated A 270 degrees, it looked exactly like B. Then it applied that same transformation to C, and generated image D. It then would compare the generated image D against the possible answers and choose the answer that has the highest similarity to generated D.

Note on the Fractal Method: The fractal method is algorithmically similar to the Affine method, but it is performed on multiple subsets of each image in order to have a more granular view of the possible transformations made between each image pair.

### Pixel-Ratio Methods

Each of the following algorithms is very similar to the Affine method as they do similar comparisons between horizontal, vertical, and diagonal elements; however, they do not apply any similitude transformations. These algorithms are advantageous because they are less CPU-intensive and they are very effective at solving problems that the affine method struggles at, such as image addition/subtraction and shape correlation problems.

• Dark Pixel Ratio (DPR): DPR takes in two images and returns the difference in the ratio of dark pixels over total pixels for each image.
• Intersection Pixel Ratio (IPR): IPR takes in two images and then returns the ratio of intersecting dark pixels between both images over the total number of dark pixels in both images.
• Non-Matching Pixel Ratio (NMPR): NMPR calculates the ratio of non-matching pixels to the total number of pixels – it is basically the inverse of IPR.
• Dark Pixel Difference (DPD): DPD calculates the difference between the proportion of total dark pixels in two images.

## Three by Three Considerations

Three-by-three problems are a major leap in complexity from two-by-two problems, as there are many more relationships between rows, columns, and diagonals compared to two-by-two problems. Here is an example of what 3×3 problems look like, and a good example of the complexity increase over the two-by-two problems.

When expanding the problems to include three-by-three problems, there are many transformations that need to be taken into account. These can be seen in the above table, which describes each type of relationship between all parts of a 3×3 RPM problem. Including more of these comparisons was vital when implementing the pixel ratio methods described above, as it led to much greater accuracy when selecting the correct answer.

## Efficacy of the Intelligence Test Solving AI Agent

After implementing each of the algorithms above, tuning them with specific weights derived from an optimization algorithm, and continuously iterating, the agent was able to solve 75 out of 96 total problems, or roughly 78% of all problems. This is slightly higher than the human average.

## Comparison to Human Cognition

While this agent was able to exceed typical human performance, it and humans do not think alike and both approach the RPM tests in different ways. The human approach to these problems revolves around propositional logic and visuospatial knowledge, while the agent’s approach is based purely on the values of pixels in images and how they relate to one another.

There are some surface-level similarities, such as how humans and the agent alike break down the problems into sub-problems by using ‘frame representations’. In addition, both apply case-based reasoning to pick the correct answer from examples. Besides these similarities, the agent’s cognition and human cognition are very dissimilar.

### References

Kunda, Maithilee (2013). “Visual problem-solving in autism, psychometrics, and AI: the case of the Raven’s Progressive Matrices intelligence test”. PhD thesis. Georgia Institute of Technology, pp. 120–120.

Kunda, Maithilee, McGreggor, Keith, and Goel, Ashok (2010). “Taking a look (literally!) at the Raven’s intelligence test: Two visual solution strategies”. In: Proceedings of the Annual Meeting of the Cognitive Science Society. Vol. 32.