Reducing Diversity to Generate Hierarchical Archetypes
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866914958632026112 |
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| author | Ibias, Alfredo Antona, Hector Ramirez-Miranda, Guillem Guinovart, Enric Alarcon, Eduard |
| author_facet | Ibias, Alfredo Antona, Hector Ramirez-Miranda, Guillem Guinovart, Enric Alarcon, Eduard |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_18633 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Reducing Diversity to Generate Hierarchical Archetypes Ibias, Alfredo Antona, Hector Ramirez-Miranda, Guillem Guinovart, Enric Alarcon, Eduard Artificial Intelligence 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. |
| title | Reducing Diversity to Generate Hierarchical Archetypes |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2409.18633 |