3 papers
cs.AI2026
A representational framework for learning and encoding structurally enriched trajectories in complex agent environments
Corina Catarau-Cotutiu, Esther Mondragon, Eduardo Alonso
The ability of artificial intelligence agents to make optimal decisions and generalise them to different domains and tasks is compromised in complex scenarios. One way to address t…
cs.AI2025
Algebras of actions in an agent's representations of the world
Alexander Dean, Eduardo Alonso, Esther Mondragon
In this paper, we propose a framework to extract the algebra of the transformations of worlds from the perspective of an agent. As a starting point, we use our framework to reprodu…
cs.NE2025
Advancing the Biological Plausibility and Efficacy of Hebbian Convolutional Neural Networks
Julian Jimenez Nimmo, Esther Mondragon
The research presented in this paper advances the integration of Hebbian learning into Convolutional Neural Networks (CNNs) for image processing, systematically exploring different…