Reducing Diversity to Generate Hierarchical Archetypes
<|reference_start|>Reducing Diversity to Generate Hierarchical Archetypes: 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.<|reference_end|>
@article{ibias2024reducing,
    title={Reducing Diversity to Generate Hierarchical Archetypes},
    author={Alfredo Ibias, Hector Antona, Guillem Ramirez-Miranda, Enric
  Guinovart, Eduard Alarcon},
    journal={arXiv preprint arXiv:2409.18633},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.18633},
    primaryClass={cs.AI}
}