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Structured Dynamics in the Algorithmic Agent.

Giulio Ruffini1, Francesca Castaldo1, Jakub Vohryzek2,3

  • 1Brain Modeling Department, Neuroelectrics, 08035 Barcelona, Spain.

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Summary
This summary is machine-generated.

Algorithmic agents use compressive models to understand the world. This study shows that tracking natural data forces agents to mirror world model symmetries, creating hierarchical structures.

Keywords:
AIKolmogorov theoryLie groups and pseudogroupsalgorithmic information theory (AIT)computational neuroscienceconservation lawscontrol theorygroupsmanifold hypothesisneural networksneurophenomenologysymmetry

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Area of Science:

  • Computational Neuroscience
  • Artificial Intelligence
  • Theoretical Physics

Background:

  • The Kolmogorov Theory of Consciousness proposes algorithmic agents use compressive models for subjective experience and action planning.
  • Understanding the dynamical principles governing these agents is crucial for advancing AI and neuroscience.

Purpose of the Study:

  • To investigate how tracking natural data influences the structure and dynamics of algorithmic agents.
  • To formalize generative models using group theory and explore their connection to agent dynamics.

Main Methods:

  • Formalized generative models using Lie pseudogroups to represent symmetries in natural data.
  • Utilized a generic neural network as a dynamical system proxy for the agent.
  • Drew parallels to Noether's theorem to link data tracking with symmetry properties.

Main Results:

  • Demonstrated that agents tracking natural data must mirror the symmetry properties of their generative world model.
  • Showed this dual constraint enforces a hierarchical organization in neural networks, aligning with the manifold hypothesis.
  • Connected concepts from algorithmic information theory, group theory, and dynamical systems.

Conclusions:

  • Findings offer insights into the neural basis of agenthood and structured experience.
  • Provides a framework for designing advanced artificial intelligence and computational brain models.
  • Highlights the role of symmetry and conservation laws in emergent agent properties.