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Maximizing the Adjacent Possible in Automata Chemistries.

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Area of Science:

  • Artificial Life
  • Theoretical Computer Science
  • Evolutionary Computation

Background:

  • Automata chemistries are complex systems requiring careful design for open-ended evolution experiments.
  • Existing models lack a clear framework for separating evolving (biological) and non-evolving (physical) components.

Purpose of the Study:

  • To develop an abstract model for classifying automata chemistry features from physical and biological perspectives.
  • To establish design principles for creating evolvable automata chemistries.

Main Methods:

  • Developed a two-level abstract model: an evolving 'biology' and a non-evolving 'physics'.
  • Defined design principles: Everything Evolves, Everything's Soft, and Everything Dies.
  • Evaluated the model and principles using experiments in the Stringmol automata chemistry.

Main Results:

  • The abstract model successfully classifies automata chemistry features.
  • Stringmol automata chemistry exhibits properties aligning with the proposed design principles.
  • Demonstrated the utility of the principles in guiding the design of automata chemistries.

Conclusions:

  • The proposed abstract model and design principles provide a robust framework for creating evolvable automata chemistries.
  • This approach aids in the systematic design and experimentation within artificial life systems.