Abstract
The Artificial Intelligence field is flooded with optimisation methods. In this paper, we change the focus to developing modelling methods with the aim of getting us closer to Artificial General Intelligence. To do so, we propose a novel way to interpret reality as an information source, that is later translated into a computational framework able to capture and represent such information. This framework is able to build elements of classical cognitive architectures, like Long Term Memory and Working Memory, starting from a simple primitive that only processes Spatial Distributed Representations. Moreover, it achieves such level of verticality in a seamless scalable hierarchical way.
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Acknowledgments
We want to thank Daniel Pinyol, Hector Antona and Pere Mayol for our insightful discussions about the topic.
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Ibias, A., Ramirez-Miranda, G., Guinovart, E., Alarcon, E. (2024). From Manifestations to Cognitive Architectures: A Scalable Framework. In: Thórisson, K.R., Isaev, P., Sheikhlar, A. (eds) Artificial General Intelligence. AGI 2024. Lecture Notes in Computer Science(), vol 14951. Springer, Cham. https://doi.org/10.1007/978-3-031-65572-2_10
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DOI: https://doi.org/10.1007/978-3-031-65572-2_10
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