Artificial life

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This article is about a field of research. For artificially created life forms, see Synthetic life. For the journal, see Artificial Life (journal).

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"ALife" redirects here. It is not to be confused with Alife.

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.[1] The discipline was named by Christopher Langton, an American computer scientist, in 1986.[2] There are three main kinds of alife,[3] named for their approaches: soft,[4] from software; hard,[5] from hardware; and wet, from biochemistry. Artificial life researchers study traditional biology by trying to recreate aspects of biological phenomena.[6]

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

Overview

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.

Philosophy

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”.[7]

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" [8]

At present, the commonly accepted definition of life does not consider any current alife simulations or software to be alive, and they do not constitute part of the evolutionary process of any ecosystem. 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.[9]
  • 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.

Organizations

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Main article: Artificial life organizations

Software-based - "soft"

Techniques

  • 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.

Notable simulators

This is a list of artificial life/digital organism simulators, organized by the method of creature definition.

Name Driven By Started Ended
Avida executable DNA 1993 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 ongoing
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

Program-based

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Further information: programming game

Program-based simulations contain organisms with a complex DNA language, usually Turing complete. This language is more often in the form of a computer program than actual biological DNA. Assembly derivatives are the most common languages used. An organism "lives" when its code is executed, and there are usually various methods allowing self-replication. Mutations are generally implemented as random changes to the code. Use of cellular automata is common but not required. Another example could be an artificial intelligence and multi-agent system/program.

Module-based

Individual modules are added to a creature. These modules modify the creature's behaviors and characteristics either directly, by hard coding into the simulation (leg type A increases speed and metabolism), or indirectly, through the emergent interactions between a creature's modules (leg type A moves up and down with a frequency of X, which interacts with other legs to create motion). Generally these are simulators which emphasize user creation and accessibility over mutation and evolution.

Parameter-based

Organisms are generally constructed with pre-defined and fixed behaviors that are controlled by various parameters that mutate. That is, each organism contains a collection of numbers or other finite parameters. Each parameter controls one or several aspects of an organism in a well-defined way.

Neural net–based

These simulations have creatures that learn and grow using neural nets or a close derivative. Emphasis is often, although not always, more on learning than on natural selection.

Hardware-based - "hard"

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Further information: Robot

Hardware-based artificial life mainly consist of robots, that is, automatically guided machines able to do tasks on their own.

Biochemical-based - "wet"

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Further information: Synthetic biology

Biochemical-based life is studied in the field of synthetic biology. It involves e.g. the creation of synthetic DNA. The term "wet" is an extension of the term "wetware".

Open problems in ALife

How does life arise from the nonliving?[10][11]
  • 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.
What are the potentials and limits of 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.
How is life related to mind, machines, and culture?
  • 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.

Related subjects

  1. Artificial intelligence has traditionally used a top down approach, while alife generally works from the bottom up.[12]
  2. Artificial chemistry started as a method within the alife community to abstract the processes of chemical reactions.
  3. Evolutionary algorithms are a practical application of the weak alife principle applied to optimization problems. 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.[citation needed] The following is a list of evolutionary algorithms closely related to and used in alife:
  4. Multi-agent system - A multi-agent system is a computerized system composed of multiple interacting intelligent agents within an environment.
  5. Evolutionary art uses techniques and methods from artificial life to create new forms of art.
  6. Evolutionary music uses similar techniques, but applied to music instead of visual art.
  7. Abiogenesis and the origin of life sometimes employ alife methodologies as well.

History

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Main article: History of artificial life

Criticism

Alife has had a controversial history. John Maynard Smith criticized certain artificial life work in 1994 as "fact-free science".[13]

See also

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2

References

  1. ↑ Lua error in package.lua at line 80: module 'strict' not found.
  2. ↑ The MIT Encyclopedia of the Cognitive Sciences, The MIT Press, p.37. ISBN 978-0-262-73144-7
  3. ↑ Lua error in package.lua at line 80: module 'strict' not found.
  4. ↑ Lua error in package.lua at line 80: module 'strict' not found.
  5. ↑ Lua error in package.lua at line 80: module 'strict' not found.
  6. ↑ Lua error in package.lua at line 80: module 'strict' not found.
  7. ↑ See Langton, C. G. 1992. Artificial Life. Addison-Wesley. ., section 1
  8. ↑ See Red'ko, V. G. 1999. Mathematical Modeling of Evolution. in: F. Heylighen, C. Joslyn and V. Turchin (editors): Principia Cybernetica Web (Principia Cybernetica, Brussels). For the importance of ALife modeling from a cosmic perspective, see also Vidal, C. 2008.The Future of Scientific Simulations: from Artificial Life to Artificial Cosmogenesis. In Death And Anti-Death, ed. Charles Tandy, 6: Thirty Years After Kurt Gödel (1906-1978) p. 285-318. Ria University Press.)
  9. ↑ Lua error in package.lua at line 80: module 'strict' not found.
  10. ↑ Lua error in package.lua at line 80: module 'strict' not found.
  11. ↑ Lua error in package.lua at line 80: module 'strict' not found.
  12. ↑ Lua error in package.lua at line 80: module 'strict' not found.
  13. ↑ Horgan, J. 1995. From Complexity to Perplexity. Scientific American. p107

External links

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