Artificial life

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Artificial life (commonly Alife or alife) is a field of study and an associated art form which examine systems related to life, its processes, and its evolution through simulations using computer models, robotics, and biochemistry.[1] There are three main kinds of alife, named for their approaches: soft, from software; hard, from hardware; and wet, from biochemistry.[2] Artificial life imitates traditional biology by trying to recreate biological phenomena.[3] The term "artificial life" is often used to specifically refer to soft alife.[citation needed]

A Braitenberg simulation, programmed in breve, an artificial life simulator


Artificial life studies the logic of living systems in artificial environments. The goal is to study the phenomena of living systems in order to come to an understanding of the complex information processing that defines such systems.

Also sometimes included in the umbrella term Artificial Life are agent based systems which are used to study the emergent properties of societies of agents.


At present, the commonly accepted definition of life does not consider any current alife simulations to be truly alive. However, different opinions about artificial life's potential have arisen:

  • 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 they 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 higher organisms that can learn. The symbiosis between learning and evolution is central to theories about the development of instincts in higher organisms, as in, for instance, the Baldwin effect.

Related subjects

  1. Artificial intelligence has traditionally used a top down approach, while alife generally works from the bottom up.[citation needed]
  2. Artificial chemistry started as a method within the alife community to abstract the processes of chemical reactions.
  3. Evolutionary algorithms applied to optimization problems are strongly related to weak alife, yet are sometimes dismissed as "not real artificial life".[citation needed] Many optimization algorithms have been crafted which borrow from or closely mirror alife techniques. The primary difference lies in explicitly defining the fitness of an agent by its ability to solve a problem, instead of its ability to find food, reproduce, or avoid death.Template:Facts The following is a list of evolutionary algorithms closely related to and used in alife:
  4. Evolutionary art uses techniques and methods from artificial life to create new forms of art.
  5. Evolutionary music uses similar techniques, but applied to music instead of visual art.



Alife has had a controversial history. John Maynard Smith criticized certain artificial life work in 1994 as "fact-free science".[4] However, the recent publication of artificial life articles in widely read journals such as Science and Nature is evidence that artificial life techniques are becoming more accepted in the mainstream, at least as a method of studying evolution.[5]

Notable simulators

Digital organism simulators

See also


  1. " definition". Retrieved 2007-01-19.
  2. Mark A. Bedau (November 2003). "Artificial life: organization, adaptation and complexity from the bottom up" (PDF). TRENDS in Cognitive Sciences. Retrieved 2007-01-19.
  3. Christopher Langton. "What is Artificial Life?". Retrieved 2007-01-19.
  4. Horgan, J. 1995. From Complexity to Perplexity. Scientific American. p107
  5. "Evolution experiments with digital organisms". Retrieved 2007-01-19.

External links

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