Prospects for machine embryogenesis

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{Pollack:2011:ECAL,
  author =       "Jordan Pollack",
  title =        "Prospects for machine embryogenesis",
  booktitle =    "Advances in Artificial Life, ECAL",
  year =         "2011",
  editor =       "Rene Doursat",
  address =      "Paris",
  month =        aug # " 8-12",
  organisation = "ISAL",
  publisher =    "MIT Press",
  note =         "Keynote",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "https://mitpress.mit.edu/sites/default/files/titles/alife/0262297140chap6.pdf",
  URL =          "http://www.cs.brandeis.edu/?p=312",
  size =         "1 page???",
  abstract =     "In Nature, the embryogenesis process proceeds from a
                 single fertilized cell through division, migration,
                 specialization and apoptosis. Although a lot is known
                 about development, we still have a long way to go from
                 theories of pattern formation towards understanding the
                 intelligence within an unsupervised manufacturing
                 process which robustly assembles complex biological
                 forms.

                 Our approach has been to co-evolve bodies and brains in
                 simulation and then convert them into reality using
                 commercial manufacturing technology. I will review
                 several generations of robots which were automatically
                 designed using co-evolutionary techniques. The goal has
                 been the fully automated design and construction of
                 artificial lifeforms.

                 The first generation was based on genetic programming
                 and a simulation of LEGO rod adhesion. The second
                 generation used direct evolution on a iterative
                 simulation of truss structures and used 3D printing for
                 the output. A third generation was based on generative
                 representations using L-systems.

                 In each of these cases, we assumed a perfect factory
                 which could accept an evolved specification and then
                 manufacture the desired result. In reality, there is no
                 perfect factory, except for the science fiction Star
                 Trek replicator. All manufacturing and assembly systems
                 are subject to error. Each primitive manufacturing
                 action results not in a deterministic new state, but a
                 probability distribution of outcomes.

                 In later work, we replaced the idea of a perfect
                 factory with one subject to noise and error. Even the
                 smallest bit of error ruins the outcome of
                 deterministic construction plans. We first evolved
                 construction plans which could overcome errors through
                 redundancy, and then this led to a new model for
                 machine embryogenesis as a process which continuously
                 optimizes assembly processes in a game against
                 Nature.",
  notes =        "http://www.ecal11.org/",
}

Genetic Programming entries for Jordan B Pollack

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