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Artificial life

Artificial life (often abbreviated ALife or A-Life) is a field of study wherein researchers examine systems related to natural life, its processes, and its evolution, through the use of simulations with computer models, robotics, and biochemistry. The discipline was named by Christopher Langton, an American theoretical biologist, in 1986. There are three main kinds of alife, named for their approaches: soft, from software; hard, from hardware; and wet, from biochemistry. Artificial life researchers study traditional biology by trying to recreate aspects of biological phenomena.

Artificial life studies the fundamental processes of living systems in artificial environments in order to gain a deeper understanding of the complex information processing that define such systems. These topics are broad, but often include evolutionary dynamics, emergent properties of collective systems, biomimicry, as well as related issues about the philosophy of the nature of life and the use of lifelike properties in artistic works.

The modeling philosophy of artificial life strongly differs from traditional modeling by studying not only "life-as-we-know-it" but also "life-as-it-might-be".

A traditional model of a biological system will focus on capturing its most important parameters. In contrast, an alife modeling approach will generally seek to decipher the most simple and general principles underlying life and implement them in a simulation. The simulation then offers the possibility to analyse new and different lifelike systems.

Vladimir Georgievich Red'ko proposed to generalize this distinction to the modeling of any process, leading to the more general distinction of "processes-as-we-know-them" and "processes-as-they-could-be".

Name Driven By Started Ended
Avida executable DNA 1993 ongoing
Neurokernel Geppetto 2014 ongoing
breve executable DNA 2006 2009
Creatures neural net/simulated biochemistry mid-1996s Fandom still active to this day
Critterding neural net 2005 ongoing
Darwinbots executable DNA 2003 ongoing
DigiHive executable DNA 2006 2009
DOSE executable DNA 2012 ongoing
EcoSim Fuzzy Cognitive Map 2009 ongoing
Evolve 4.0 executable DNA 1996 Prior to Nov. 2014
Framsticks executable DNA 1996 ongoing
Noble Ape neural net 1996 ongoing
OpenWorm Geppetto 2011 ongoing
Polyworld neural net 1990 ongoing
Primordial Life executable DNA 1994 2003
ScriptBots executable DNA 2010 ongoing
TechnoSphere modules 1995
Tierra executable DNA 1991 2004
3D Virtual Creature Evolution neural net 2008 NA

How does life arise from the nonliving?
What are the potentials and limits of living systems?
How is life related to mind, machines, and culture?
  • The strong alife (cf. Strong AI) position states that "life is a process which can be abstracted away from any particular medium" (John von Neumann). Notably, Tom Ray declared that his program Tierra is not simulating life in a computer but synthesizing it.
  • The weak alife position denies the possibility of generating a "living process" outside of a chemical solution. Its researchers try instead to simulate life processes to understand the underlying mechanics of biological phenomena.
  • Cellular automata were used in the early days of artificial life, and are still often used for ease of scalability and parallelization. Alife and cellular automata share a closely tied history.
  • Neural networks are sometimes used to model the brain of an agent. Although traditionally more of an artificial intelligence technique, neural nets can be important for simulating population dynamics of organisms that can learn. The symbiosis between learning and evolution is central to theories about the development of instincts in organisms with higher neurological complexity, as in, for instance, the Baldwin effect.
  • Generate a molecular proto-organism in vitro.
  • Achieve the transition to life in an artificial chemistry in silico.
  • Determine whether fundamentally novel living organizations can exist.
  • Simulate a unicellular organism over its entire life cycle.
  • Explain how rules and symbols are generated from physical dynamics in living systems.
  • Determine what is inevitable in the open-ended evolution of life.
  • Determine minimal conditions for evolutionary transitions from specific to generic response systems.
  • Create a formal framework for synthesizing dynamical hierarchies at all scales.
  • Determine the predictability of evolutionary consequences of manipulating organisms and ecosystems.
  • Develop a theory of information processing, information flow, and information generation for evolving systems.
  • Demonstrate the emergence of intelligence and mind in an artificial living system.
  • Evaluate the influence of machines on the next major evolutionary transition of life.
  • Provide a quantitative model of the interplay between cultural and biological evolution.
  • Establish ethical principles for artificial life.


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