1 INTRODUCTION  
1.1 Motivation and scope  
1.2 Related work  
1.3 Structure  

2 RAVEN’S PROGRESSIVE MATRICES  
2.1 Problem statement  
2.2 Automatic generation of RPMs  

3 LEARNING TO SOLVE RPMs  
3.1 Supervised training  
3.2 Auxiliary training  
3.3 Contrastive training  
3.4 Learning with an optimal trajectory  
3.5 Data augmentation  
3.8 Unsupervised learning  
3.6 Disentangled representations  
3.7 Generative modeling  

4 RPM DEEP LEARNING MODELS  
4.1 Baselines  
4.2 Relational reasoning networks  
4.3 Hierarchical networks  

5 EVALUATING MACHINE INTELLIGENCE WITH RPMs  
5.1 Results on PGM  
5.2 Results on (I-)RAVEN  
5.3 Model sizes  

6 DISCUSSION  
6.1 Seeds and fruits of RPM research  
6.2 Challenges and open problems  

7 CONCLUSION