Category theory framework proposed for defining and comparing AGI architectures

This working paper proposes a formal framework for comparing different Artificial General Intelligence (AGI) architectures using category theory. The authors aim to provide a unified foundation for AGI systems, integrating aspects like structure, information organization, and agent interaction. The framework is intended to clarify commonalities and differences between various AGI approaches, such as Reinforcement Learning and Active Inference, and to guide future research. AI

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IMPACT Offers a novel theoretical lens for comparing diverse AGI architectures, potentially unifying research efforts.

RANK_REASON This is a working paper published on arXiv proposing a theoretical framework for AGI. [lever_c_demoted from research: ic=1 ai=1.0]

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• AGI
• Category Theory
• Reinforcement Learning
• Active Inference
• Pablo De Los Riscos

• paper
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