+ Energy By: Chelsea Bonollo, Meg Hemmerlein, Josh Salzberg.
Heredity, Complexity and Surprise: Embedded Self-Replication and Evolution in CA Chris Salzberg 1,2...
-
Upload
emery-gilbert -
Category
Documents
-
view
229 -
download
0
Transcript of Heredity, Complexity and Surprise: Embedded Self-Replication and Evolution in CA Chris Salzberg 1,2...
Heredity, Complexity and Surprise:Embedded Self-Replication
and Evolution in CA
Chris Salzberg1,2 and Hiroki Sayama1
1 Dept. of Human Communication, University of Electro-Communications, Japan2 Graduate School of Arts and Sciences, University of Tokyo, Japan
Summary
• Introduction: History of embedded models of self-replication
in cellular automata
• Concepts: Embeddedness Explicitness Heredity Evolutionary growth of complexity
• Evolvable self-replicators in CA• Conclusions
Self-replication and ALife
• Self-replication is one of the main themes of research in Artificial Life.
• In the past, research has mainly targeted regulated behavior: Universal construction, Self-replication, Self-inspection, Functionality.
• Behavior oriented toward pre-defined goals.
von Neumann’s theory
• von Neumann was inspired by the many increases of “complication” observed in natural organisms.
• His “Theory of Self-Reproducing Automata”: proved that such increases could in
principle be realized in artificial automata, outlined a concrete example of such a
constructive automata in a 29-state CA.
Some key features
• Uses a discrete cellular space with local rules (as suggested by S. Ulam)
• Introduces separation between passive tape and active machine: evolution occurs via mutations to tape, construction pathways exist from simpler
to more complex types (McMullin,2000).
• CA rules are fixed during evolution.
The key issue
• System is computationally intractable: requires 29 states and a highly complex
set of transition rules, occupies an estimated 50,000 to
200,000 CA cells (Sipper,1998).• Extremely sensitive to
perturbations (non-robust, brittle).• Only recently simulated for the first
time (Pesavento,1995).
Solutions to this “problem”
• Demand so-called “non-trivial” self-reproduction (rather than universality): some minimal level of structural
complexity, and a translation/transcription process that is
highly explicit.
• These criteria make no demands on heredity.
A Popular Example
• Langton (Langton,1984) designed the self-reproducing loop (SR Loop): uses a much smaller set of rules, requires only a few hundred cells, and is readily realizable.
• However, the SR loop cannot accommodate mutations.
• Hence, it cannot evolve (no heredity).
von Neumann’s definition
• “[S]elf-reproduction includes the ability to undergo inheritable mutations as well as the ability to make another organism like the original” (von Neumann,1949).
• The capacity to withstand viable hereditary mutations was central to von Neumann’s formal theory.
“Marginal” heredity?
• Do there exist simple CA-based self-replicating structures that: span an infinite and diverse space of
possible genotypes/phenotypes, are able to withstand viable hereditary
mutations, and evolve spontaneously via physical laws
rather than any explicit mutation operator?
Embeddedness
• Quantifies the extent to which state information of an individual is expressed in the arena of competition.
• Embeddedness enables “the very structure of the individual to be modified”, likely a necessary condition for open-ended evolution (Taylor,1999).
Embeddedness of systems
• CA are highly embedded: They do not “hide” any information
(except the transition rules), and allow for direct and unrestricted
interactions between cells.
• Systems of evolutionary computer programs (e.g. (Ray,1991)) are less so: Most information is hidden in auxiliary
non-interactive locations (memory).
Embeddedness and materiality
• Self-replicators embedded in CA share an important feature with biological organisms: Both are built up from, and interact
through, a common material structure grounded in physical laws (i.e. CA rules).
• This makes them “messy” to analyze.• But also potentially rich in dynamics.
Explicitness
• Degree to which a self-replication process is governed by an environment rather than an object in that environment (Taylor,1999).
• e.g. explicitness of translation and transcription (Langton,1984).
• Often used as criterion for non-trivial self-replication (somewhat arbitrary).
Heredity
• Heredity is a more appropriate criteria: Distinguishes simple replicators (e.g. SR
Loop) from potentially evolvable machines (e.g. von Neumann’s UC).
Focuses on static descriptions rather than translation/transcription process,
Potentially enables “reproduction without degeneration in size or level of organization” (von Neumann,1949).
Growth of complexity
• Principle conditions for the “evolutionary growth of complexity” (McMullin,2000): Exhibit a concrete class of machines
that are “purely mechanistic”, “show that they span a significant
range of complexity”, and “demonstrate that there are
construction pathways leading from the simplest to the most complex”.
von Neumann’s insight
• von Neumann discovered a system which satisfies these conditions, but: It is extremely complicated, and It is extremely fragile/brittle.
• In addition: It enables a mutational growth of
complexity (construction pathways), but
It does not necessarily enable a Darwinian growth of complexity.
Practical alternatives
• Can we find simpler CA-based self-replicators of marginal hereditary and structural complexity, which concretely realize these criteria?
• What evolutionary complexity growth, if any, do we observe in these CA?
Marginal CA Replicators
• Many self-replicating structures have been implemented in CA.
• Most of these CA target regulated behavior (functional or computational capabilities).
• A small subset, however, were designed with the aim of studying the evolutionary process itself.
Outline of observations
• Observed behaviors: Emergence of self-replicators from a
“soup” of parts (Chou & Reggia,1997) Spontaneous evolution (Sayama,1999) Genetic diversity, complex genealogy,
complexity-increase (Salzberg et al.,2004) Structural variability & complexity-
increase (Suzuki & Ikegami, 2003) Spontaneous evolution of robust self-
replicators (Azpeitia and Ibanez, 2002) Template-based replication (Hutton, 2003)
Categorization of self-reps
• To categorize CA models, we use a method by Taylor (Taylor,1999): 2D visualization scheme x-axis = copy process (explicit/implicit) y-axis = heredity (limited/indefinite)
• Central region represents self-replicators of marginal hereditary and structural complexity.
Categorization of self-reps
Heredity
Copying Process
limited
indefinite
explicit(structure-based)
implicit(physics-based)
minimal self-reps(Langton ‘84, etc.)
emergent self-reps(Chou & Reggia, ‘97)
evoloop (Sayama, ‘99)
robust self-inspectioncellular replicators(Azpeitia et al., 2002)
von Neumann’s self-repAutomata (1950s)
template-basedself-reps in CA
(Hutton ‘02, etc.)
interaction-basedevolving loops
(Suzuki et al., ‘03)
gene-transmittingworms (Sayama, ‘00)
symbioorganisms(Barricelli ‘57)
‘trivial’self-reps)
Conclusions
• Complexity-increase of a limited kind is possible in practice.
• Marginal replicators can realize: High levels of hereditary variability Structural robustness Spontaneous (Darwinian) evolution
• Such models constitute the first step towards von Neumann’s original goal of complexity-increase in CA.
References
• I. Azpeitia and J. Ibanez. Spontaneous emergence of robust cellular replicators. In S. Bandini, B. Chopard, and M. Tomassini, editors, Fifth International Conference on Cellular Automata for Research and Industry (ACRI 2002), pages 132-143. Springer, 2002.
• H.H. Chou and J.A. Reggia. Emergence of self-replicating structures in a cellular automata space. Physica D, 110:252-276, 1997.
• T.J. Hutton. Evolvable self-replicating molecules in an artificial chemistry. Arificial Life, 8:341-356, 2002.• C.G. Langton. Self-reproduction in cellular automata. Physica D, 10:135-144, 1984.• B. McMullin. John von Neumann and the evolutionary growth of complexity: Looking backward, looking forward…
Artificial Life, 6:347-361, 2000.• U. Pesavento. An implementation of von Neumann’s self-reproducing machine. Artifiical Life, 2:335-352, 1996.• T.S. Ray. An approach to the synthesis of life. In Artificial Life II, volume XI of SFI Studies on the Sciences of
Complexity, pages 371-408. Addison-Wesley Publishing Company, Redwood City, California, 1991.• C. Salzberg, A. Antony, and H. Sayama. Evolutionary dynamics of cellular automata-based self-replicators in hostile
environments. BioSystems. In press.• H. Sayama. A new structurally dissolvable self-reproducing loop evolving in a simple cellular automata space.
Artificial Life, 5:343-365, 1999.• H. Sayama. Self-replicating worms that increase structural complexity through gene transmission. In M.A. Bedau,
J.S. McCaskill, N.H. Packard, and S. Rasmussen, editors, Artificial Life VII: Proceedings of the Seventh International Conference on Artificial Life. MIT Press, 2000.
• M. Sipper. Fifty years of research on self-replication: An overview. Artificial Life, 4:237-257, 1998.• K. Suzuki and T. Ikegami. Interaction based evolution of self-replicating loop structures. In Proceedings of the
Seventh European Conference on Artificial Life, pages 89-93, Dortmund, Germany, 2003.• T.J. Taylor. From artificial evolution to artificial life. PhD thesis, University of Edinburgh, 1999.• J. von Neumann. Re-evaluation of the problems of complicated automata - problems of hierarchy and evolution
(Fifth Illinois Lecture), December 1949. In W. Aspray and A. Burks, editors, Papers of John von Neumann on Computing and Computer Theory, pages 477-490. MIT Press, 1987.