Steffen Illium

PhD. in Comp. Science
AI Consultant & Researcher


Reference

  1. Illium, S., Zorn, M., Lenta, C., Kölle, M., Linnhoff-Popien, C., and Gabor, T. 2022. Constructing organism networks from collaborative self-replicators. 2022 IEEE Symposium Series on Computational Intelligence (SSCI), IEEE, 1268–1275.

This research investigates the transition from simple self-replication to higher levels of organization by exploring how populations of basic, self-replicating neural network units can form “Organism Networks” (ONs) through collaboration and emergent differentiation. Moving beyond the replication of individual networks, the focus shifts to the collective dynamics and functional capabilities that arise when these units interact within a shared environment (akin to an “artificial chemistry”).

Diagram showing individual self-replicating units interacting to form a larger Organism Network structure
Conceptual architecture of an Organism Network emerging from interacting self-replicators.


The core hypothesis is that through local interactions and potentially shared environmental feedback, initially homogeneous populations of self-replicators can spontaneously develop specialized roles or structures, leading to a collective entity with capabilities exceeding those of individual units.

Visualization potentially related to network robustness, differentiation, or communication channels.

Key aspects explored in this work include:

  • Mechanisms for Collaboration: Investigating how communication or resource sharing between self-replicating units can be established and influence collective behavior.
  • Emergent Differentiation: Analyzing scenarios where units within the population begin to specialize, adopting different internal states (weight configurations) or functions, analogous to cellular differentiation in biological organisms.
  • Formation of Structure: Studying how interactions lead to stable spatial or functional structures within the population, forming the basis of the Organism Network.
  • Functional Advantages: Assessing whether these emergent ONs exhibit novel collective functionalities or improved problem-solving capabilities compared to non-interacting populations. (The role of dropout, as suggested by the image, might relate to promoting robustness or specialization within this context).

This study bridges the gap between single-unit self-replication and the emergence of complex, multi-unit systems in artificial life research, offering insights into how collaborative dynamics can lead to higher-order computational structures. For more detailed insights, refer to [Illium et al. 2022].

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