# Reducing Diversity to Generate Hierarchical Archetypes

arXiv: https://arxiv.org/abs/2409.18633 · HTML: https://mlsanity.com/paper/2409.18633/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 23 references that are in the map.
- First posted on arXiv 27 Sep 2024.

## Builds on (most cited first, 5 of 5)

- [Safe, Multi-Agent, Reinforcement Learning for Autonomous Driving](https://mlsanity.com/paper/1610.03295/) (2016)
- [A Deep Hierarchical Approach to Lifelong Learning in Minecraft](https://mlsanity.com/paper/1604.07255/) (2016)
- [Do Convolutional Neural Networks Learn Class Hierarchy?](https://mlsanity.com/paper/1710.06501/) (2017)
- [Towards Understanding Hierarchical Learning: Benefits of Neural Representations](https://mlsanity.com/paper/2006.13436/) (2020)
- [When does Diversity Help Generalization in Classification Ensembles?](https://mlsanity.com/paper/1910.13631/) (2019)

## Abstract (arXiv)

The Artificial Intelligence field seldom address the development of a fundamental building piece: a framework, methodology or algorithm to automatically build hierarchies of abstractions. This is a key requirement in order to build intelligent behaviour, as recent neuroscience studies clearly expose. In this paper we present a primitive-based framework to automatically generate hierarchies of constructive archetypes, as a theory of how to generate hierarchies of abstractions. We assume the existence of a primitive with very specific characteristics, and we develop our framework over it. We prove the effectiveness of our framework through mathematical definitions and proofs. Finally, we give a few insights about potential uses of our framework and the expected results.