Kinematic Self-Replicating Machines
© 2004 Robert A. Freitas Jr. and Ralph C. Merkle. All Rights Reserved.
Robert A. Freitas Jr., Ralph C. Merkle, Kinematic Self-Replicating Machines, Landes Bioscience, Georgetown, TX, 2004.
5.1.5 Sipper POE Model of Bio-Inspired Hardware Systems (1997)
Moshe Sipper and colleagues [384, 535, 2430-2434] have devised a simple classification scheme for artificial life (ALife) systems that may be useful for some classes of artificial replicators as well. In the authors’ words: “Living organisms are complex systems exhibiting a range of desirable characteristics, such as evolution, adaptation, and fault tolerance, that have proved difficult to realize using traditional engineering methodologies. Recently, engineers have been allured by certain natural processes, giving birth to such domains as artificial neural networks and evolutionary computation. If one considers life on Earth since its very beginning, then the following three levels of organization can be distinguished.”
(1) Phylogeny. “The first level concerns the temporal evolution of the genetic program, the hallmark of which is the evolution of species, or phylogeny. The multiplication of living organisms is based upon the reproduction of the program, subject to an extremely low error rate at the individual level, so as to ensure that the identity of the offspring remains practically unchanged. Mutation (asexual reproduction) or mutation along with recombination (sexual reproduction) give rise to the emergence of new organisms. The phylogenetic mechanisms are fundamentally non-deterministic, with the mutation and recombination rate providing a major source of diversity. This diversity is indispensable for the survival of living species, for their continuous adaptation to a changing environment, and for the appearance of new species.”
(2) Ontogeny. “Upon the appearance of multicellular organisms, a second level of biological organization manifests itself. The successive divisions of the mother cell, the zygote, with each newly formed cell possessing a copy of the original genome, is followed by a specialization of the daughter cells in accordance with their surroundings, i.e., their position within the ensemble. This latter phase is known as cellular differentiation. Ontogeny is thus the developmental process of a multicellular organism. This process is essentially deterministic: an error in a single base within the genome can provoke an ontogenetic sequence which results in notable, possibly lethal, malformations.”
(3) Epigenesis. “The ontogenetic program is limited in the amount of information that can be stored, thereby rendering the complete specification of the organism impossible. A well-known example is that of the human brain with some 1010 neurons and 1014 connections, far too large a number to be completely specified in the four-character genome of length approximately 3 x 109. Therefore, upon reaching a certain level of complexity, there must emerge a different process that permits the individual to integrate the vast quantity of interactions with the outside world. This process is known as epigenesis, and primarily includes the nervous system, the immune system, and the endocrine system. These systems are characterized by the possession of a basic structure that is entirely defined by the genome (the innate part), which is then subjected to modification through lifetime interactions of the individual with the environment (the acquired part). The epigenetic processes can be loosely grouped under the heading of learning systems.”
By analogy to nature, the design space of bio-inspired hardware systems can be partitioned along the three axes of phylogeny, ontogeny, and epigenesis (the POE model [2430-2432]; Figure 5.2) with the axes defined as follows: the phylogenetic axis involves evolution; the ontogenetic axis involves the development of a single individual from its own genetic material, essentially without environmental interactions; and the epigenetic axis involves learning through environmental interactions that take place after formation of the individual.
For example , hardware implementations of the following three paradigms can be positioned along the POE axes as follows: (1) evolutionary algorithms are the simplified artificial counterpart of phylogeny (“P” axis”) in nature; (2) multicellular automata are based on the concept of ontogeny (“O” axis), where a single mother cell gives rise, through multiple divisions, to a multicellular organism; and (3) artificial neural networks embody the epigenetic process (“E” axis), where the system’s synaptic weights and perhaps topological structure change through interactions with the environment.
Last updated on 1 August 2005