5 papers
Categorical Internalisation of Environmental Groupoids for Generalisable POMDP Solving
Ben Opperman, Eduardo Alonso, Esther Mondragón
This paper advocates category theory as a practical framework for structuring and improving rein- forcement learning in high-dimensional, partially observable environments. We mode…
Groupoid-Based Internal State Representations for Reinforcement Learning with Local Symmetries
Ben Opperman, Eduardo Alonso, Esther Mondragón
Symmetries play a central role in reducing the complexity of reinforcement learning problems, yet most existing approaches rely on fixed group actions or predefined state abstracti…
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…
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…
AIGenC: An AI generalisation model via creativity
Corina Catarau-Cotutiu, Esther Mondragon, Eduardo Alonso
Inspired by cognitive theories of creativity, this paper introduces a computational model (AIGenC) that lays down the necessary components to enable artificial agents to learn, use…