Designing a New Class of Autopoietic Machines

EasyChair Preprint 6260

Abstract

All living organisms are autopoietic and cognitive. Autopoiesis refers to a system with well-defined identity and is capable of reproducing and maintaining itself. Cognition, on the other hand, is the ability to process information, apply knowledge, and change the circumstance A living organism is a unique autonomous system made up of components and relationships changing over time without changing the unity of the system. The genome contains the knowledge that is required to build the components using physical and chemical processes and physical resources. Information processing structures in the form of genes and neurons provide the means to build, operate and manage the stability of the system while interacting with the external world where the interactions are often, non-deterministic in nature and are subject to large fluctuations. Our understanding of how theses information processing structures operate comes from the analysis of the genome, experiments in neuroscience and the studies of cognitive behaviors in living organisms. First, we summarize here, the key learnings that point to how the organism manages both the “self” and its interactions with external world. Second, we use the new mathematics of structural machines, triadic automata, knowledge structures, named sets and cognizing oracle theories to present a model that captures the key features of autopoietic behavior. Finally, we use this model to design digital autopoietic machines by defining a digital genome that uses digital genes (in the form of executable algorithms) and digital neuronal models (in the form of deep learning neural networks) as information processing structures.

Keyphrases: Autopoietic Machines, Computing models, Structural Machines, autopoiesis, cognition, deep learning, knowledge structures

@booklet{EasyChair:6260,
  author    = {Rao Mikkilineni and Giovanni Morana},
  title     = {Designing a New Class of Autopoietic Machines},
  howpublished = {EasyChair Preprint 6260},
  year      = {EasyChair, 2021}}