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Moved page from wikipedia:en:Artificial life (history)
此词条暂由彩云小译翻译，未经人工整理和审校，带来阅读不便，请见谅。{{short description|A field of study wherein researchers examine systems related to natural life, its processes, and its evolution, through the use of simulations}}
{{About|a field of research|artificially created life forms|Synthetic life|the journal|Artificial Life (journal)}}
{{Redirect-distinguish|ALife|Alife (disambiguation){{!}}Alife}}
'''Artificial life''' (often abbreviated '''ALife''' or '''A-Life''') is a field of study wherein researchers examine [[system]]s related to natural [[life]], its processes, and its evolution, through the use of [[simulation]]s with [[computer models]], [[robotics]], and [[biochemistry]].<ref>{{cite web|url=http://dictionary.reference.com/browse/artificial%20life|title=Dictionary.com definition|accessdate=2007-01-19}}</ref> The discipline was named by [[Christopher Langton]], an American theoretical biologist, in 1986.<ref>[https://books.google.com/books?id=-wt1aZrGXLYC&pg=PA37&cd=1#v=onepage The MIT Encyclopedia of the Cognitive Sciences], The MIT Press, p.37. {{ISBN|978-0-262-73144-7}}</ref> In 1987 Langton organized the first conference on the field, in [[Los Alamos, New Mexico]].<ref>{{cite magazine |title=The Game Industry's Dr. Frankenstein |magazine=[[Next Generation (magazine)|Next Generation]]|issue=35|publisher=[[Imagine Media]] |date=November 1997|page=10}}</ref> There are three main kinds of alife,<ref>{{cite web |url=http://www.reed.edu/~mab/publications/papers/BedauTICS03.pdf|title=Artificial life: organization, adaptation and complexity from the bottom up|author=Mark A. Bedau |date=November 2003|accessdate=2007-01-19|publisher=Trends in Cognitive Sciences|archive-url=https://web.archive.org/web/20081202185445/http://www.reed.edu/~mab/publications/papers/BedauTICS03.pdf|archive-date=2008-12-02|url-status=dead}}</ref> named for their approaches: ''soft'',<ref>{{cite book|title=Artificial Life Models in Software |author=Maciej Komosinski and [[Andrew Adamatzky]]|year=2009|publisher=Springer |location=New York|isbn=978-1-84882-284-9|url=https://www.springer.com/computer/mathematics/book/978-1-84882-284-9}}</ref> from [[software]]; ''hard'',<ref>{{cite book |title=Artificial Life Models in Hardware|author=[[Andrew Adamatzky]] and Maciej Komosinski|year=2009|publisher=Springer|location=New York|isbn=978-1-84882-529-1 |url=https://www.springer.com/computer/hardware/book/978-1-84882-529-1}}</ref> from [[computer hardware|hardware]]; and ''[[wet artificial life|wet]]'', from biochemistry. Artificial life researchers study traditional [[biology]] by trying to recreate aspects of biological phenomena.<ref>{{cite web|url=http://zooland.alife.org/|title=What is Artificial Life?|first=Christopher|last=Langton|accessdate=2007-01-19 |archiveurl=https://web.archive.org/web/20070117220840/http://zooland.alife.org/|archivedate=2007-01-17|url-status=dead}}</ref><ref>Aguilar, W., Santamaría-Bonfil, G., Froese, T., and Gershenson, C. (2014). The past, present, and future of artificial life. Frontiers in Robotics and AI, 1(8). https://dx.doi.org/10.3389/frobt.2014.00008</ref>
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. In 1987 Langton organized the first conference on the field, in Los Alamos, New Mexico. 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.
人工生命(通常缩写为 ALife 或 A-Life)是一个研究领域，研究人员通过使用计算机模型、机器人和生物化学模拟来研究与自然生命、其过程和进化相关的系统。这个学科在1986年由美国理论生物学家克里斯托弗·兰顿命名。1987年，朗顿在洛斯阿拉莫斯组织了第一次这方面的会议。生命有三种主要的方式，因其方式而得名: 软源于软件; 硬源于硬件; 湿源于生物化学。人工生命研究者通过试图再现生物现象的某些方面来研究传统生物学。
[[File:Braitenberg vehicle (simulation made with breve).jpg|thumb|right|250px|A [[Braitenberg vehicle]] simulation, programmed in breve, an artificial life simulator]]
A [[Braitenberg vehicle simulation, programmed in breve, an artificial life simulator]]
一个[布莱登伯格飞行器模拟器，在短期内编程，一个人工生命模拟器]
== Overview ==
== Overview ==
概览
Artificial life studies the fundamental processes of [[living system]]s 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 algorithm|evolutionary dynamics]], [[Emergence|emergent properties of collective systems]], [[Biomimetics|biomimicry]], as well as related issues about the [[Philosophy of biology|philosophy of the nature of life]] and the use of lifelike properties in artistic works.
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.
人工生命研究人工环境中生命系统的基本过程，以便对定义这些系统的复杂信息处理有更深入的理解。这些话题很广泛，但通常包括进化动力学，集体系统的涌现特性，仿生学，以及有关生命本质的哲学和在艺术作品中使用逼真特性的相关问题。
== Philosophy ==
== Philosophy ==
哲学
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".<ref>See Langton, C. G. 1992. [http://www.probelog.com/texts/Langton_al.pdf Artificial Life] {{webarchive |url=https://web.archive.org/web/20070311225322/http://www.probelog.com/texts/Langton_al.pdf |date=March 11, 2007 }}. Addison-Wesley. ., section 1</ref>
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".
人工生命的建模哲学不仅研究“我们所知的生命” ，而且研究“可能的生命” ，这与传统的建模哲学有着很大的不同。
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.
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.
传统的生物系统模型将着重于捕捉其最重要的参数。相比之下，人生建模方法通常寻求破译生活中最简单和最一般的原则，并在模拟中实现它们。这种模拟为分析新的和不同的生物系统提供了可能性。
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".<ref>See Red'ko, V. G. 1999. [http://pespmc1.vub.ac.be/MATHME.html 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.[https://arxiv.org/abs/0803.1087 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.)</ref>
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".
Vladimir Georgievich Red‘ ko 建议将这种区分概括为对任何过程的建模，从而导致对”我们所知道的过程”和”过程可能是的过程”的更一般性区分。
At present, the commonly accepted [[Life#Definitions|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:
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:
目前，普遍接受的生命定义并不认为任何现有的生命模拟或软件是活的，它们也不构成任何生态系统进化过程的一部分。然而，关于人工生命的潜力出现了不同的观点:
* The ''strong alife'' (cf. [[philosophy of artificial intelligence#Strong AI vs. weak AI|Strong AI]]) position states that "life is a process which can be abstracted away from any particular medium" ([[John von Neumann]]) {{citation needed|date=April 2018|reason=There seems to be no original source for this quote, and in fact if you restrict a web search to pages that don't contain the phrase artificial life, there are only a handful left.}}. Notably, [[Thomas S. Ray|Tom Ray]] declared that his program [[Tierra (computer simulation)|Tierra]] is not simulating life in a computer but synthesizing it.<ref>{{cite journal|last1=Ray|first1=Thomas|authorlink1=Thomas S. Ray|editor1-last=Taylor|editor1-first=C. C.|editor2-last=Farmer|editor2-first=J. D.|editor3-last=Rasmussen|editor3-first=S|title=An approach to the synthesis of life|journal=Artificial Life II, Santa Fe Institute Studies in the Sciences of Complexity|date=1991|volume=XI|pages=371–408|url=http://life.ou.edu/pubs/alife2/tierra.tex|language=English|quote=The intent of this work is to synthesize rather than simulate life.|archiveurl=https://web.archive.org/web/20150711051305/http://life.ou.edu/pubs/alife2/tierra.tex|archivedate=2015-07-11|accessdate=24 January 2016|url-status=live}}</ref>
* 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.
==Software-based ("soft")==
==Software-based ("soft")==
基于软件的(“软”)
=== Techniques ===
=== Techniques ===
=== Techniques ===
*[[cellular automaton|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.
*[[Artificial neural network]]s 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 ===
=== Notable simulators ===
值得注意的模拟器
This is a list of artificial life/[[digital organism]] simulators, organized by the method of creature definition.
This is a list of artificial life/digital organism simulators, organized by the method of creature definition.
这是一个人工生命 / 数字有机体模拟器的列表，按照生物定义的方法组织。
{| class="wikitable sortable"
{| class="wikitable sortable"
{ | class“ wikitable sortable”
|-
|-
|-
! Name !! Driven By !! Started !! Ended
! Name !! Driven By !! Started !! Ended
!姓名! ！驱动器！开始! ！结束
|-
|-
|-
| ApeSDK (formerly Noble Ape) || language/social simulation || 1996 || ongoing
| ApeSDK (formerly Noble Ape) || language/social simulation || 1996 || ongoing
语言 / 社会模拟 | 1996 | 进行中
|-
|-
|-
| [[Avida]] || executable DNA || 1993 || ongoing
| Avida || executable DNA || 1993 || ongoing
可执行 DNA 1993年正在进行中
|-
|-
|-
| Biogenesis || executable DNA || 2006 || ongoing
| Biogenesis || executable DNA || 2006 || ongoing
生物起源 | 可执行 DNA | 2006 | | 正在进行
|-
|-
|-
|Neurokernel
|Neurokernel
|Neurokernel
|Geppetto
|Geppetto
| 格培多
|2014
|2014
|2014
|ongoing
|ongoing
正在进行中
|-
|-
|-
| [[Creatures (artificial life program)|Creatures]] || neural net/simulated biochemistry || 1996-2001 || Fandom still active to this day, some abortive attempts at new products
| Creatures || neural net/simulated biochemistry || 1996-2001 || Fandom still active to this day, some abortive attempts at new products
生物 | 神经网络 / 模拟生物化学 | 1996-2001 | 狂热者至今仍然活跃，一些新产品的失败尝试
|-
|-
|-
| Critterding || neural net || 2005 || ongoing
| Critterding || neural net || 2005 || ongoing
2005年正在进行中
|-
|-
|-
| Darwinbots || executable DNA || 2003 || ongoing
| Darwinbots || executable DNA || 2003 || ongoing
达尔文机器人可执行的 DNA 正在进行中
|-
|-
|-
| DigiHive || executable DNA || 2006 || ongoing
| DigiHive || executable DNA || 2006 || ongoing
可执行 DNA 2006年正在进行
|-
|-
|-
| DOSE || executable DNA || 2012 || ongoing
| DOSE || executable DNA || 2012 || ongoing
可执行 DNA | 2012 | | 正在进行
|-
|-
|-
| [[EcoSim]] || Fuzzy Cognitive Map || 2009 || ongoing
| EcoSim || Fuzzy Cognitive Map || 2009 || ongoing
| EcoSim | | 模糊认知地图 | | 2009 | | 正在进行中
|-
|-
|-
| [[Framsticks]] || executable DNA || 1996 || ongoing
| Framsticks || executable DNA || 1996 || ongoing
可执行 DNA | 1996 | 正在进行
|-
|-
|-
| Geb || neural net || 1997 || ongoing
| Geb || neural net || 1997 || ongoing
1997年正在进行
|-
|-
|-
| [[OpenWorm]] || Geppetto || 2011 || ongoing
| OpenWorm || Geppetto || 2011 || ongoing
2011年3月11日
|-
|-
|-
| [[Polyworld]] || neural net || 1990 || ongoing
| Polyworld || neural net || 1990 || ongoing
多元世界 | 神经网 | 1990 | | 正在进行
|-
|-
|-
| Primordial Life || executable DNA || 1994 || 2003
| Primordial Life || executable DNA || 1994 || 2003
原始生命 | 可执行 DNA | 1994 | 2003
|-
|-
|-
| ScriptBots || executable DNA || 2010 || ongoing
| ScriptBots || executable DNA || 2010 || ongoing
| ScriptBots | | 可执行 DNA | | 2010 | | | 持续
|-
|-
|-
| [[TechnoSphere]] || modules || 1995 ||
| TechnoSphere || modules || 1995 ||
1995年
|-
|-
|-
| [[Tierra (computer simulation)|Tierra]] || executable DNA || 1991 || 2004
| Tierra || executable DNA || 1991 || 2004
可执行 DNA | 1991 | 2004
|-
|-
|-
| [[3D Virtual Creature Evolution]] || neural net || 2008 || NA
| 3D Virtual Creature Evolution || neural net || 2008 || NA
3 d 虚拟生物进化神经网络
|}
|}
|}
====Program-based====
====Program-based====
基于程序的
{{Further|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|multi-agent system/program]].
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.
基于程序的模拟包含具有复杂 DNA 语言的生物体，通常是图灵完成。这种语言通常以计算机程序的形式出现，而不是真正的生物 DNA。汇编导数是最常用的语言。一个有机体在代码执行的时候是“活着”的，通常有各种各样的方法允许自我复制。变异通常是作为代码的随机变更来实现的。使用细胞自动机是常见的，但不是必需的。另一个例子可能是人工智能和多 agent 系统 / 程序。
====Module-based====
====Module-based====
基于模块的
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.
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.
单个模块被添加到一个生物上。这些模块直接通过硬编码进入模拟(腿 a 型增加速度和新陈代谢) ，或者间接通过生物模块之间的紧急互动(腿 a 型上下移动频率为 x，与其他腿交互创造运动)来修改生物的行为和特征。一般来说，这些模拟器强调的是用户创造和可访问性，而不是突变和进化。
====Parameter-based====
====Parameter-based====
基于参数的
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.
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.
生物体通常是由预先定义和固定的行为构成的，这些行为受各种变异参数的控制。也就是说，每个生物体包含一组数字或其他有限参数。每个参数都以一种明确的方式控制一个生物体的一个或几个方面。
====Neural net–based====
====Neural net–based====
基于神经网络的
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.
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.
这些模拟让生物通过神经网络或近似衍生物进行学习和成长。通常强调的是学习，而不是自然选择，尽管并不总是如此。
===Complex systems modeling===
===Complex systems modeling===
复杂的系统建模
Mathematical models of complex systems are of three types: [[black-box]] (phenomenological), [[White box (software engineering)|white-box]] (mechanistic, based on the [[first principles]]) and [[Grey box model|grey-box]] (mixtures of phenomenological and mechanistic models).<ref name="Kalmykov Lev V., Kalmykov Vyacheslav L. Solution">
Mathematical models of complex systems are of three types: black-box (phenomenological), white-box (mechanistic, based on the first principles) and grey-box (mixtures of phenomenological and mechanistic models).<ref name="Kalmykov Lev V., Kalmykov Vyacheslav L. Solution">
复杂系统的数学模型有三种类型: 黑箱(现象学)、白箱(基于第一性原理的机制)和灰箱(现象学与机制模型的混合)。 卡尔米科夫列夫 v，卡尔米科夫维切斯拉夫解决方案
{{Citation
{{Citation
{引文
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最后的卡尔米科夫
| first = Lev V.
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第一个列夫 v。
| last2 = Kalmykov
| last2 = Kalmykov
2卡尔米科夫
| first2 = Vyacheslav L.
| first2 = Vyacheslav L.
| first2 Vyacheslav l.
| title = A Solution to the Biodiversity Paradox by Logical Deterministic Cellular Automata
| title = A Solution to the Biodiversity Paradox by Logical Deterministic Cellular Automata
用逻辑确定性细胞自动机解决生物多样性悖论
| journal = Acta Biotheoretica
| journal = Acta Biotheoretica
生物理论学学报
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第63卷
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第二期
| pages = 1–19
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第1-19页
| year = 2015
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2015年
| doi = 10.1007/s10441-015-9257-9
| doi = 10.1007/s10441-015-9257-9
10.1007 / s10441-015-9257-9
| pmid = 25980478
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25980478
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} / ref name"kalmykov lev v，kalmykov vyacheslav l. white-box model"{ Citation
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最后的卡尔米科夫
| first = Lev V.
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第一个列夫 v。
| last2 = Kalmykov
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2卡尔米科夫
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| first2 Vyacheslav l.
| title = A white-box model of S-shaped and double S-shaped single-species population growth
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S 形和双 s 形单种群增长的白盒模型
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26038717
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4451025
}}</ref> In black-box models, the individual-based (mechanistic) mechanisms of a complex dynamic system remain hidden. [[File:Mathematical models for complex systems.jpg|thumb|Mathematical models for complex systems]] Black-box models are completely nonmechanistic. They are phenomenological and ignore a composition and internal structure of a complex system. We cannot investigate interactions of subsystems of such a non-transparent model. A white-box model of complex dynamic system has ‘transparent walls’ and directly shows underlying mechanisms. All events at micro-, meso- and macro-levels of a dynamic system are directly visible at all stages of its white-box model evolution. In most cases mathematical modelers use the heavy black-box mathematical methods, which cannot produce mechanistic models of complex dynamic systems. Grey-box models are intermediate and combine black-box and white-box approaches. [[File:Logical deterministic individual-based cellular automata model of single species population growth.gif|thumb|Logical deterministic individual-based cellular automata model of single species population growth]] Creation of a white-box model of complex system is associated with the problem of the necessity of an a priori basic knowledge of the modeling subject. The deterministic logical [[Cellular automaton|cellular automata]] are necessary but not sufficient condition of a white-box model. The second necessary prerequisite of a white-box model is the presence of the physical [[ontology]] of the object under study. The white-box modeling represents an automatic hyper-logical inference from the [[first principle]]s because it is completely based on the deterministic logic and axiomatic theory of the subject. The purpose of the white-box modeling is to derive from the basic axioms a more detailed, more concrete mechanistic knowledge about the dynamics of the object under study. The necessity to formulate an intrinsic [[axiomatic system]] of the subject before creating its white-box model distinguishes the cellular automata models of white-box type from cellular automata models based on arbitrary logical rules. If cellular automata rules have not been formulated from the first principles of the subject, then such a model may have a weak relevance to the real problem.<ref name="Kalmykov Lev V., Kalmykov Vyacheslav L. White-box model" />
}}</ref> In black-box models, the individual-based (mechanistic) mechanisms of a complex dynamic system remain hidden. Mathematical models for complex systems Black-box models are completely nonmechanistic. They are phenomenological and ignore a composition and internal structure of a complex system. We cannot investigate interactions of subsystems of such a non-transparent model. A white-box model of complex dynamic system has ‘transparent walls’ and directly shows underlying mechanisms. All events at micro-, meso- and macro-levels of a dynamic system are directly visible at all stages of its white-box model evolution. In most cases mathematical modelers use the heavy black-box mathematical methods, which cannot produce mechanistic models of complex dynamic systems. Grey-box models are intermediate and combine black-box and white-box approaches. Logical deterministic individual-based cellular automata model of single species population growth Creation of a white-box model of complex system is associated with the problem of the necessity of an a priori basic knowledge of the modeling subject. The deterministic logical cellular automata are necessary but not sufficient condition of a white-box model. The second necessary prerequisite of a white-box model is the presence of the physical ontology of the object under study. The white-box modeling represents an automatic hyper-logical inference from the first principles because it is completely based on the deterministic logic and axiomatic theory of the subject. The purpose of the white-box modeling is to derive from the basic axioms a more detailed, more concrete mechanistic knowledge about the dynamics of the object under study. The necessity to formulate an intrinsic axiomatic system of the subject before creating its white-box model distinguishes the cellular automata models of white-box type from cellular automata models based on arbitrary logical rules. If cellular automata rules have not been formulated from the first principles of the subject, then such a model may have a weak relevance to the real problem.
} / ref 在黑盒模型中，复杂动态系统中基于个体的(机制)机制仍然是隐藏的。复杂系统的数学模型黑箱模型是完全非机械的。它们是现象学的，忽略了复杂系统的组成和内部结构。我们不能研究这样一个非透明模型的子系统之间的相互作用。复杂动态系统的白盒子模型具有“透明墙” ，直接揭示了内在机制。一个动态系统的微观、中观和宏观层面的所有事件在其白盒模型演化的所有阶段都是直接可见的。在大多数情况下，数学模型使用沉重的黑箱数学方法，不能产生复杂动态系统的机械模型。灰盒模型是中间的，结合了黑盒和白盒方法。单种群增长的逻辑确定性个体元胞自动机模型复杂系统的白盒模型的产生与建模主体先验基础知识的必要性问题有关。确定性逻辑元胞自动机是白盒模型存在的必要条件，但不是充分条件。白盒模型的第二个必要前提是被研究对象的物理本体的存在。白盒建模代表了基于第一原则的自动超逻辑推理，因为它完全基于主体的确定性逻辑和公理系统。白盒建模的目的是从基本公理推导出关于被研究对象的动力学的更详细、更具体的机械知识。在创建其白盒模型之前，必须确定主体的内在公理系统，这使得白盒模型区别于基于任意逻辑规则的细胞自动机模型。如果细胞自动机规则没有从主题的第一原则制定，那么这样的模型可能有一个弱相关性的实际问题。
[[File:Logical deterministic individual-based cellular automata model of interspecific competition for a single limited resource.gif|thumb|Logical deterministic individual-based cellular automata model of interspecific competition for a single limited resource]]
Logical deterministic individual-based cellular automata model of interspecific competition for a single limited resource
对于单个有限资源，基于逻辑确定性个体的种间竞争元胞自动机模型
==Hardware-based ("hard")==
==Hardware-based ("hard")==
基于硬件(“硬件”)
{{Further|Robot}}
Hardware-based artificial life mainly consist of ''robots'', that is, [[automation|automatically]] guided [[machine]]s able to do tasks on their own.
Hardware-based artificial life mainly consist of robots, that is, automatically guided machines able to do tasks on their own.
基于硬件的人工生命主要由机器人组成，即能够自动引导机器完成任务的机器人。
==Biochemical-based ("wet")==
==Biochemical-based ("wet")==
生物化学为基础(“湿”)
{{Further|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 (brain)|wetware]]".
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".
生物化学为基础的生命是在合成生物学领域研究。它涉及到。合成 DNA 的创造。“湿”一词是“湿件”一词的延伸。
In May 2019, researchers, in a milestone effort, reported the creation of a new [[Synthetic biology#Synthetic life|synthetic]] (possibly [[Artificial life#Biochemical-based ("wet")|artificial]]) form of [[wikt:viability|viable]] [[life]], a variant of the [[bacteria]] ''[[Escherichia coli]]'', by reducing the natural number of 64 [[codon]]s in the bacterial [[genome]] to 59 codons instead, in order to encode 20 [[amino acid]]s.<ref name="NYT-20190515">{{cite news |last=Zimmer |first=Carl |authorlink=Carl Zimmer |title=Scientists Created Bacteria With a Synthetic Genome. Is This Artificial Life? - In a milestone for synthetic biology, colonies of E. coli thrive with DNA constructed from scratch by humans, not nature. |url=https://www.nytimes.com/2019/05/15/science/synthetic-genome-bacteria.html |date=15 May 2019 |work=[[The New York Times]] |accessdate=16 May 2019 }}</ref><ref name="NAT-20190515">{{cite journal |author=Fredens, Julius |display-authors=et al. |title=Total synthesis of Escherichia coli with a recoded genome |date=15 May 2019 |journal=[[Nature (journal)|Nature]] |doi=10.1038/s41586-019-1192-5 |pmid=31092918 |pmc=7039709 |volume=569 |issue=7757 |pages=514–518 }}</ref>
In May 2019, researchers, in a milestone effort, reported the creation of a new synthetic (possibly artificial) form of viable life, a variant of the bacteria Escherichia coli, by reducing the natural number of 64 codons in the bacterial genome to 59 codons instead, in order to encode 20 amino acids.
2019年5月，研究人员在一项具有里程碑意义的工作中报告了一种新的合成的(可能是人工的)有生命的生命形式的创造，它是大肠桿菌的变种，通过将细菌基因组中64个密码子的自然数目减少到59个密码子来编码20个氨基酸。
== Open problems ==
== Open problems ==
开放性问题
;How does life arise from the nonliving?<ref>{{cite web|url=http://libarynth.org/open_problems_in_alife|title=Libarynth|accessdate=2015-05-11}}</ref><ref>{{cite web|url=http://authors.library.caltech.edu/13564/1/BEDal00.pdf|title=Caltech |accessdate=2015-05-11}}</ref>
How does life arise from the nonliving?
生命是如何从无生命中产生的？
*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?
What are the potentials and limits of living systems?
生命系统的潜力和限制是什么？
*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 (information theory)|information flow]], and information generation for evolving systems.
;How is life related to mind, machines, and culture?
How is life related to mind, machines, and culture?
生命是如何与思想、机器和文化联系起来的？
*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 ==
== Related subjects ==
相关科目
#[[Artificial intelligence]] has traditionally used a [[Top-down and bottom-up design|top down]] approach, while alife generally works from the bottom up.<ref>{{cite web |url=http://lggwg.com/wolff/aicg99/stern.html |title=AI Beyond Computer Games |accessdate=2008-07-04 |archiveurl=https://web.archive.org/web/20080701040911/http://www.lggwg.com/wolff/aicg99/stern.html |archivedate=2008-07-01 |url-status=dead }}</ref>
Artificial intelligence has traditionally used a top down approach, while alife generally works from the bottom up.
人工智能传统上使用自上而下的方法，而生活通常是自下而上的。
#[[Artificial chemistry]] started as a method within the alife community to abstract the processes of chemical reactions.
Artificial chemistry started as a method within the alife community to abstract the processes of chemical reactions.
人工化学最初是作为生命群体中抽象化学反应过程的一种方法而出现的。
#[[Evolutionary algorithm]]s are a practical application of the weak alife principle applied to [[optimization problem]]s. 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|date=August 2007}} The following is a list of evolutionary algorithms closely related to and used in alife:
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. The following is a list of evolutionary algorithms closely related to and used in alife:
进化算法是弱生命原理在优化问题中的实际应用。许多优化算法是借鉴或模仿现实生活技术而精心设计的。主要的区别在于明确定义一个代理人的适合性，根据其解决问题的能力，而不是它的能力，找到食物，繁殖，或避免死亡。以下是一系列与生活密切相关并用于生活的进化算法:
#*[[Ant colony optimization]]
*Ant colony optimization
* 蚁群优化
#*[[Bacterial colony optimization]]
*Bacterial colony optimization
* 细菌菌落优化
#*[[Genetic algorithm]]
*Genetic algorithm
* 遗传算法
#*[[Genetic programming]]
*Genetic programming
* 遗传程式设计
#*[[Swarm intelligence]]
*Swarm intelligence
* 群体智能
#[[Multi-agent system]] – A multi-agent system is a computerized system composed of multiple interacting intelligent agents within an environment.
Multi-agent system – A multi-agent system is a computerized system composed of multiple interacting intelligent agents within an environment.
多智能体系统-多智能体系统是一个计算机化的系统，由一个环境中多个相互作用的智能代理组成。
#[[Evolutionary art]] uses techniques and methods from artificial life to create new forms of art.
Evolutionary art uses techniques and methods from artificial life to create new forms of art.
进化艺术使用人工生命的技术和方法来创造新的艺术形式。
#[[Evolutionary music]] uses similar techniques, but applied to music instead of visual art.
Evolutionary music uses similar techniques, but applied to music instead of visual art.
进化的音乐使用了类似的技术，但是应用于音乐而不是视觉艺术。
#[[Abiogenesis]] and the origin of life sometimes employ alife [[methodology|methodologies]] as well.
Abiogenesis and the origin of life sometimes employ alife methodologies as well.
自然发生和生命起源有时也使用生命学方法。
== History ==
== History ==
历史
{{Main|History of artificial life}}
== Criticism ==
== Criticism ==
批评
Alife has had a controversial history. [[John Maynard Smith]] criticized certain artificial life work in 1994 as "fact-free science".<ref>Horgan, J. 1995. [http://www2.econ.iastate.edu/tesfatsi/hogan.complexperplex.htm From Complexity to Perplexity]. Scientific American. p107</ref>
Alife has had a controversial history. John Maynard Smith criticized certain artificial life work in 1994 as "fact-free science".
阿里夫有一段颇具争议的历史。约翰·梅纳德·史密斯在1994年批评某些人工生命工作是“无事实的科学”。
== See also ==
== See also ==
参见
{{Portal|Systems science}}
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*{{annotated link|Artificial consciousness}}
*{{annotated link|Applications of artificial intelligence}}
*{{annotated link|Autonomous robot}}
*{{annotated link|Bioethics}}
*{{annotated link|Complex adaptive system}}
*{{annotated link|Darwin machine}}
*{{annotated link|Digital morphogenesis}}
*{{annotated link|Emergence}}
*{{annotated link|Life simulation game}}
*{{annotated link|List of emerging technologies}}
*{{annotated link|Mathematical and theoretical biology}}
*{{annotated link|Multi-agent system}}
*{{annotated link|Outline of artificial intelligence}}
*{{annotated link|Player Project}}
*{{annotated link|Simulated reality}}
*{{annotated link|Social simulation}}
*{{annotated link|Soda Constructor}}
*{{annotated link|Swarm intelligence}}
*{{annotated link|Synthetic life}}
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==References==
==References==
参考资料
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See http://en.wikipedia.org/wiki/Wikipedia:Footnotes for a
参见《 http://en.wikipedia.org/wiki/wikipedia:footnotes
discussion of different citation methods and how to generate
discussion of different citation methods and how to generate
讨论不同的引用方法和如何产生
footnotes using the <ref>, </ref> and <reference /> tags
footnotes using the and tags
使用 and 标签的脚注
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{{Reflist}}
==External links==
==External links==
外部链接
*{{DMOZ|Computers/Artificial_Life}}
*[http://alife.org/ International Society of Artificial Life]
*[http://www.mitpressjournals.org/loi/artl ''Artificial Life''] journal, at MIT Press Journal
*[http://www.envirtech.com/artificial-life-lab.html The Artificial Life Lab], a virtual environment lab
{{Computer science}}
{{Authority control}}
{{DEFAULTSORT:Artificial Life}}
[[Category:Artificial life| ]]
[[Category:Scientific modeling]]
Category:Scientific modeling
类别: 科学建模
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<small>This page was moved from [[wikipedia:en:Artificial life]]. Its edit history can be viewed at [[人工生命/edithistory]]</small></noinclude>
[[Category:待整理页面]]
{{About|a field of research|artificially created life forms|Synthetic life|the journal|Artificial Life (journal)}}
{{Redirect-distinguish|ALife|Alife (disambiguation){{!}}Alife}}
'''Artificial life''' (often abbreviated '''ALife''' or '''A-Life''') is a field of study wherein researchers examine [[system]]s related to natural [[life]], its processes, and its evolution, through the use of [[simulation]]s with [[computer models]], [[robotics]], and [[biochemistry]].<ref>{{cite web|url=http://dictionary.reference.com/browse/artificial%20life|title=Dictionary.com definition|accessdate=2007-01-19}}</ref> The discipline was named by [[Christopher Langton]], an American theoretical biologist, in 1986.<ref>[https://books.google.com/books?id=-wt1aZrGXLYC&pg=PA37&cd=1#v=onepage The MIT Encyclopedia of the Cognitive Sciences], The MIT Press, p.37. {{ISBN|978-0-262-73144-7}}</ref> In 1987 Langton organized the first conference on the field, in [[Los Alamos, New Mexico]].<ref>{{cite magazine |title=The Game Industry's Dr. Frankenstein |magazine=[[Next Generation (magazine)|Next Generation]]|issue=35|publisher=[[Imagine Media]] |date=November 1997|page=10}}</ref> There are three main kinds of alife,<ref>{{cite web |url=http://www.reed.edu/~mab/publications/papers/BedauTICS03.pdf|title=Artificial life: organization, adaptation and complexity from the bottom up|author=Mark A. Bedau |date=November 2003|accessdate=2007-01-19|publisher=Trends in Cognitive Sciences|archive-url=https://web.archive.org/web/20081202185445/http://www.reed.edu/~mab/publications/papers/BedauTICS03.pdf|archive-date=2008-12-02|url-status=dead}}</ref> named for their approaches: ''soft'',<ref>{{cite book|title=Artificial Life Models in Software |author=Maciej Komosinski and [[Andrew Adamatzky]]|year=2009|publisher=Springer |location=New York|isbn=978-1-84882-284-9|url=https://www.springer.com/computer/mathematics/book/978-1-84882-284-9}}</ref> from [[software]]; ''hard'',<ref>{{cite book |title=Artificial Life Models in Hardware|author=[[Andrew Adamatzky]] and Maciej Komosinski|year=2009|publisher=Springer|location=New York|isbn=978-1-84882-529-1 |url=https://www.springer.com/computer/hardware/book/978-1-84882-529-1}}</ref> from [[computer hardware|hardware]]; and ''[[wet artificial life|wet]]'', from biochemistry. Artificial life researchers study traditional [[biology]] by trying to recreate aspects of biological phenomena.<ref>{{cite web|url=http://zooland.alife.org/|title=What is Artificial Life?|first=Christopher|last=Langton|accessdate=2007-01-19 |archiveurl=https://web.archive.org/web/20070117220840/http://zooland.alife.org/|archivedate=2007-01-17|url-status=dead}}</ref><ref>Aguilar, W., Santamaría-Bonfil, G., Froese, T., and Gershenson, C. (2014). The past, present, and future of artificial life. Frontiers in Robotics and AI, 1(8). https://dx.doi.org/10.3389/frobt.2014.00008</ref>
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. In 1987 Langton organized the first conference on the field, in Los Alamos, New Mexico. 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.
人工生命(通常缩写为 ALife 或 A-Life)是一个研究领域，研究人员通过使用计算机模型、机器人和生物化学模拟来研究与自然生命、其过程和进化相关的系统。这个学科在1986年由美国理论生物学家克里斯托弗·兰顿命名。1987年，朗顿在洛斯阿拉莫斯组织了第一次这方面的会议。生命有三种主要的方式，因其方式而得名: 软源于软件; 硬源于硬件; 湿源于生物化学。人工生命研究者通过试图再现生物现象的某些方面来研究传统生物学。
[[File:Braitenberg vehicle (simulation made with breve).jpg|thumb|right|250px|A [[Braitenberg vehicle]] simulation, programmed in breve, an artificial life simulator]]
A [[Braitenberg vehicle simulation, programmed in breve, an artificial life simulator]]
一个[布莱登伯格飞行器模拟器，在短期内编程，一个人工生命模拟器]
== Overview ==
== Overview ==
概览
Artificial life studies the fundamental processes of [[living system]]s 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 algorithm|evolutionary dynamics]], [[Emergence|emergent properties of collective systems]], [[Biomimetics|biomimicry]], as well as related issues about the [[Philosophy of biology|philosophy of the nature of life]] and the use of lifelike properties in artistic works.
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.
人工生命研究人工环境中生命系统的基本过程，以便对定义这些系统的复杂信息处理有更深入的理解。这些话题很广泛，但通常包括进化动力学，集体系统的涌现特性，仿生学，以及有关生命本质的哲学和在艺术作品中使用逼真特性的相关问题。
== Philosophy ==
== Philosophy ==
哲学
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".<ref>See Langton, C. G. 1992. [http://www.probelog.com/texts/Langton_al.pdf Artificial Life] {{webarchive |url=https://web.archive.org/web/20070311225322/http://www.probelog.com/texts/Langton_al.pdf |date=March 11, 2007 }}. Addison-Wesley. ., section 1</ref>
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".
人工生命的建模哲学不仅研究“我们所知的生命” ，而且研究“可能的生命” ，这与传统的建模哲学有着很大的不同。
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.
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.
传统的生物系统模型将着重于捕捉其最重要的参数。相比之下，人生建模方法通常寻求破译生活中最简单和最一般的原则，并在模拟中实现它们。这种模拟为分析新的和不同的生物系统提供了可能性。
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".<ref>See Red'ko, V. G. 1999. [http://pespmc1.vub.ac.be/MATHME.html 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.[https://arxiv.org/abs/0803.1087 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.)</ref>
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".
Vladimir Georgievich Red‘ ko 建议将这种区分概括为对任何过程的建模，从而导致对”我们所知道的过程”和”过程可能是的过程”的更一般性区分。
At present, the commonly accepted [[Life#Definitions|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:
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:
目前，普遍接受的生命定义并不认为任何现有的生命模拟或软件是活的，它们也不构成任何生态系统进化过程的一部分。然而，关于人工生命的潜力出现了不同的观点:
* The ''strong alife'' (cf. [[philosophy of artificial intelligence#Strong AI vs. weak AI|Strong AI]]) position states that "life is a process which can be abstracted away from any particular medium" ([[John von Neumann]]) {{citation needed|date=April 2018|reason=There seems to be no original source for this quote, and in fact if you restrict a web search to pages that don't contain the phrase artificial life, there are only a handful left.}}. Notably, [[Thomas S. Ray|Tom Ray]] declared that his program [[Tierra (computer simulation)|Tierra]] is not simulating life in a computer but synthesizing it.<ref>{{cite journal|last1=Ray|first1=Thomas|authorlink1=Thomas S. Ray|editor1-last=Taylor|editor1-first=C. C.|editor2-last=Farmer|editor2-first=J. D.|editor3-last=Rasmussen|editor3-first=S|title=An approach to the synthesis of life|journal=Artificial Life II, Santa Fe Institute Studies in the Sciences of Complexity|date=1991|volume=XI|pages=371–408|url=http://life.ou.edu/pubs/alife2/tierra.tex|language=English|quote=The intent of this work is to synthesize rather than simulate life.|archiveurl=https://web.archive.org/web/20150711051305/http://life.ou.edu/pubs/alife2/tierra.tex|archivedate=2015-07-11|accessdate=24 January 2016|url-status=live}}</ref>
* 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.
==Software-based ("soft")==
==Software-based ("soft")==
基于软件的(“软”)
=== Techniques ===
=== Techniques ===
=== Techniques ===
*[[cellular automaton|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.
*[[Artificial neural network]]s 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 ===
=== Notable simulators ===
值得注意的模拟器
This is a list of artificial life/[[digital organism]] simulators, organized by the method of creature definition.
This is a list of artificial life/digital organism simulators, organized by the method of creature definition.
这是一个人工生命 / 数字有机体模拟器的列表，按照生物定义的方法组织。
{| class="wikitable sortable"
{| class="wikitable sortable"
{ | class“ wikitable sortable”
|-
|-
|-
! Name !! Driven By !! Started !! Ended
! Name !! Driven By !! Started !! Ended
!姓名! ！驱动器！开始! ！结束
|-
|-
|-
| ApeSDK (formerly Noble Ape) || language/social simulation || 1996 || ongoing
| ApeSDK (formerly Noble Ape) || language/social simulation || 1996 || ongoing
语言 / 社会模拟 | 1996 | 进行中
|-
|-
|-
| [[Avida]] || executable DNA || 1993 || ongoing
| Avida || executable DNA || 1993 || ongoing
可执行 DNA 1993年正在进行中
|-
|-
|-
| Biogenesis || executable DNA || 2006 || ongoing
| Biogenesis || executable DNA || 2006 || ongoing
生物起源 | 可执行 DNA | 2006 | | 正在进行
|-
|-
|-
|Neurokernel
|Neurokernel
|Neurokernel
|Geppetto
|Geppetto
| 格培多
|2014
|2014
|2014
|ongoing
|ongoing
正在进行中
|-
|-
|-
| [[Creatures (artificial life program)|Creatures]] || neural net/simulated biochemistry || 1996-2001 || Fandom still active to this day, some abortive attempts at new products
| Creatures || neural net/simulated biochemistry || 1996-2001 || Fandom still active to this day, some abortive attempts at new products
生物 | 神经网络 / 模拟生物化学 | 1996-2001 | 狂热者至今仍然活跃，一些新产品的失败尝试
|-
|-
|-
| Critterding || neural net || 2005 || ongoing
| Critterding || neural net || 2005 || ongoing
2005年正在进行中
|-
|-
|-
| Darwinbots || executable DNA || 2003 || ongoing
| Darwinbots || executable DNA || 2003 || ongoing
达尔文机器人可执行的 DNA 正在进行中
|-
|-
|-
| DigiHive || executable DNA || 2006 || ongoing
| DigiHive || executable DNA || 2006 || ongoing
可执行 DNA 2006年正在进行
|-
|-
|-
| DOSE || executable DNA || 2012 || ongoing
| DOSE || executable DNA || 2012 || ongoing
可执行 DNA | 2012 | | 正在进行
|-
|-
|-
| [[EcoSim]] || Fuzzy Cognitive Map || 2009 || ongoing
| EcoSim || Fuzzy Cognitive Map || 2009 || ongoing
| EcoSim | | 模糊认知地图 | | 2009 | | 正在进行中
|-
|-
|-
| [[Framsticks]] || executable DNA || 1996 || ongoing
| Framsticks || executable DNA || 1996 || ongoing
可执行 DNA | 1996 | 正在进行
|-
|-
|-
| Geb || neural net || 1997 || ongoing
| Geb || neural net || 1997 || ongoing
1997年正在进行
|-
|-
|-
| [[OpenWorm]] || Geppetto || 2011 || ongoing
| OpenWorm || Geppetto || 2011 || ongoing
2011年3月11日
|-
|-
|-
| [[Polyworld]] || neural net || 1990 || ongoing
| Polyworld || neural net || 1990 || ongoing
多元世界 | 神经网 | 1990 | | 正在进行
|-
|-
|-
| Primordial Life || executable DNA || 1994 || 2003
| Primordial Life || executable DNA || 1994 || 2003
原始生命 | 可执行 DNA | 1994 | 2003
|-
|-
|-
| ScriptBots || executable DNA || 2010 || ongoing
| ScriptBots || executable DNA || 2010 || ongoing
| ScriptBots | | 可执行 DNA | | 2010 | | | 持续
|-
|-
|-
| [[TechnoSphere]] || modules || 1995 ||
| TechnoSphere || modules || 1995 ||
1995年
|-
|-
|-
| [[Tierra (computer simulation)|Tierra]] || executable DNA || 1991 || 2004
| Tierra || executable DNA || 1991 || 2004
可执行 DNA | 1991 | 2004
|-
|-
|-
| [[3D Virtual Creature Evolution]] || neural net || 2008 || NA
| 3D Virtual Creature Evolution || neural net || 2008 || NA
3 d 虚拟生物进化神经网络
|}
|}
|}
====Program-based====
====Program-based====
基于程序的
{{Further|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|multi-agent system/program]].
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.
基于程序的模拟包含具有复杂 DNA 语言的生物体，通常是图灵完成。这种语言通常以计算机程序的形式出现，而不是真正的生物 DNA。汇编导数是最常用的语言。一个有机体在代码执行的时候是“活着”的，通常有各种各样的方法允许自我复制。变异通常是作为代码的随机变更来实现的。使用细胞自动机是常见的，但不是必需的。另一个例子可能是人工智能和多 agent 系统 / 程序。
====Module-based====
====Module-based====
基于模块的
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.
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.
单个模块被添加到一个生物上。这些模块直接通过硬编码进入模拟(腿 a 型增加速度和新陈代谢) ，或者间接通过生物模块之间的紧急互动(腿 a 型上下移动频率为 x，与其他腿交互创造运动)来修改生物的行为和特征。一般来说，这些模拟器强调的是用户创造和可访问性，而不是突变和进化。
====Parameter-based====
====Parameter-based====
基于参数的
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.
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.
生物体通常是由预先定义和固定的行为构成的，这些行为受各种变异参数的控制。也就是说，每个生物体包含一组数字或其他有限参数。每个参数都以一种明确的方式控制一个生物体的一个或几个方面。
====Neural net–based====
====Neural net–based====
基于神经网络的
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.
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.
这些模拟让生物通过神经网络或近似衍生物进行学习和成长。通常强调的是学习，而不是自然选择，尽管并不总是如此。
===Complex systems modeling===
===Complex systems modeling===
复杂的系统建模
Mathematical models of complex systems are of three types: [[black-box]] (phenomenological), [[White box (software engineering)|white-box]] (mechanistic, based on the [[first principles]]) and [[Grey box model|grey-box]] (mixtures of phenomenological and mechanistic models).<ref name="Kalmykov Lev V., Kalmykov Vyacheslav L. Solution">
Mathematical models of complex systems are of three types: black-box (phenomenological), white-box (mechanistic, based on the first principles) and grey-box (mixtures of phenomenological and mechanistic models).<ref name="Kalmykov Lev V., Kalmykov Vyacheslav L. Solution">
复杂系统的数学模型有三种类型: 黑箱(现象学)、白箱(基于第一性原理的机制)和灰箱(现象学与机制模型的混合)。 卡尔米科夫列夫 v，卡尔米科夫维切斯拉夫解决方案
{{Citation
{{Citation
{引文
| last = Kalmykov
| last = Kalmykov
最后的卡尔米科夫
| first = Lev V.
| first = Lev V.
第一个列夫 v。
| last2 = Kalmykov
| last2 = Kalmykov
2卡尔米科夫
| first2 = Vyacheslav L.
| first2 = Vyacheslav L.
| first2 Vyacheslav l.
| title = A Solution to the Biodiversity Paradox by Logical Deterministic Cellular Automata
| title = A Solution to the Biodiversity Paradox by Logical Deterministic Cellular Automata
用逻辑确定性细胞自动机解决生物多样性悖论
| journal = Acta Biotheoretica
| journal = Acta Biotheoretica
生物理论学学报
| volume = 63
| volume = 63
第63卷
| issue = 2
| issue = 2
第二期
| pages = 1–19
| pages = 1–19
第1-19页
| year = 2015
| year = 2015
2015年
| doi = 10.1007/s10441-015-9257-9
| doi = 10.1007/s10441-015-9257-9
10.1007 / s10441-015-9257-9
| pmid = 25980478
| pmid = 25980478
25980478
}}</ref><ref name="Kalmykov Lev V., Kalmykov Vyacheslav L. White-box model">{{Citation
}}</ref><ref name="Kalmykov Lev V., Kalmykov Vyacheslav L. White-box model">{{Citation
} / ref name"kalmykov lev v，kalmykov vyacheslav l. white-box model"{ Citation
| last = Kalmykov
| last = Kalmykov
最后的卡尔米科夫
| first = Lev V.
| first = Lev V.
第一个列夫 v。
| last2 = Kalmykov
| last2 = Kalmykov
2卡尔米科夫
| first2 = Vyacheslav L.
| first2 = Vyacheslav L.
| first2 Vyacheslav l.
| title = A white-box model of S-shaped and double S-shaped single-species population growth
| title = A white-box model of S-shaped and double S-shaped single-species population growth
S 形和双 s 形单种群增长的白盒模型
| journal = PeerJ
| journal = PeerJ
2012年3月24日
| volume = 3:e948
| volume = 3:e948
第三卷，第948集
| pages = e948
| pages = e948
第九季，第48集
| year = 2015
| year = 2015
2015年
| doi = 10.7717/peerj.948
| doi = 10.7717/peerj.948
10.7717 / peerj. 948
| pmid = 26038717
| pmid = 26038717
26038717
| pmc = 4451025
| pmc = 4451025
4451025
}}</ref> In black-box models, the individual-based (mechanistic) mechanisms of a complex dynamic system remain hidden. [[File:Mathematical models for complex systems.jpg|thumb|Mathematical models for complex systems]] Black-box models are completely nonmechanistic. They are phenomenological and ignore a composition and internal structure of a complex system. We cannot investigate interactions of subsystems of such a non-transparent model. A white-box model of complex dynamic system has ‘transparent walls’ and directly shows underlying mechanisms. All events at micro-, meso- and macro-levels of a dynamic system are directly visible at all stages of its white-box model evolution. In most cases mathematical modelers use the heavy black-box mathematical methods, which cannot produce mechanistic models of complex dynamic systems. Grey-box models are intermediate and combine black-box and white-box approaches. [[File:Logical deterministic individual-based cellular automata model of single species population growth.gif|thumb|Logical deterministic individual-based cellular automata model of single species population growth]] Creation of a white-box model of complex system is associated with the problem of the necessity of an a priori basic knowledge of the modeling subject. The deterministic logical [[Cellular automaton|cellular automata]] are necessary but not sufficient condition of a white-box model. The second necessary prerequisite of a white-box model is the presence of the physical [[ontology]] of the object under study. The white-box modeling represents an automatic hyper-logical inference from the [[first principle]]s because it is completely based on the deterministic logic and axiomatic theory of the subject. The purpose of the white-box modeling is to derive from the basic axioms a more detailed, more concrete mechanistic knowledge about the dynamics of the object under study. The necessity to formulate an intrinsic [[axiomatic system]] of the subject before creating its white-box model distinguishes the cellular automata models of white-box type from cellular automata models based on arbitrary logical rules. If cellular automata rules have not been formulated from the first principles of the subject, then such a model may have a weak relevance to the real problem.<ref name="Kalmykov Lev V., Kalmykov Vyacheslav L. White-box model" />
}}</ref> In black-box models, the individual-based (mechanistic) mechanisms of a complex dynamic system remain hidden. Mathematical models for complex systems Black-box models are completely nonmechanistic. They are phenomenological and ignore a composition and internal structure of a complex system. We cannot investigate interactions of subsystems of such a non-transparent model. A white-box model of complex dynamic system has ‘transparent walls’ and directly shows underlying mechanisms. All events at micro-, meso- and macro-levels of a dynamic system are directly visible at all stages of its white-box model evolution. In most cases mathematical modelers use the heavy black-box mathematical methods, which cannot produce mechanistic models of complex dynamic systems. Grey-box models are intermediate and combine black-box and white-box approaches. Logical deterministic individual-based cellular automata model of single species population growth Creation of a white-box model of complex system is associated with the problem of the necessity of an a priori basic knowledge of the modeling subject. The deterministic logical cellular automata are necessary but not sufficient condition of a white-box model. The second necessary prerequisite of a white-box model is the presence of the physical ontology of the object under study. The white-box modeling represents an automatic hyper-logical inference from the first principles because it is completely based on the deterministic logic and axiomatic theory of the subject. The purpose of the white-box modeling is to derive from the basic axioms a more detailed, more concrete mechanistic knowledge about the dynamics of the object under study. The necessity to formulate an intrinsic axiomatic system of the subject before creating its white-box model distinguishes the cellular automata models of white-box type from cellular automata models based on arbitrary logical rules. If cellular automata rules have not been formulated from the first principles of the subject, then such a model may have a weak relevance to the real problem.
} / ref 在黑盒模型中，复杂动态系统中基于个体的(机制)机制仍然是隐藏的。复杂系统的数学模型黑箱模型是完全非机械的。它们是现象学的，忽略了复杂系统的组成和内部结构。我们不能研究这样一个非透明模型的子系统之间的相互作用。复杂动态系统的白盒子模型具有“透明墙” ，直接揭示了内在机制。一个动态系统的微观、中观和宏观层面的所有事件在其白盒模型演化的所有阶段都是直接可见的。在大多数情况下，数学模型使用沉重的黑箱数学方法，不能产生复杂动态系统的机械模型。灰盒模型是中间的，结合了黑盒和白盒方法。单种群增长的逻辑确定性个体元胞自动机模型复杂系统的白盒模型的产生与建模主体先验基础知识的必要性问题有关。确定性逻辑元胞自动机是白盒模型存在的必要条件，但不是充分条件。白盒模型的第二个必要前提是被研究对象的物理本体的存在。白盒建模代表了基于第一原则的自动超逻辑推理，因为它完全基于主体的确定性逻辑和公理系统。白盒建模的目的是从基本公理推导出关于被研究对象的动力学的更详细、更具体的机械知识。在创建其白盒模型之前，必须确定主体的内在公理系统，这使得白盒模型区别于基于任意逻辑规则的细胞自动机模型。如果细胞自动机规则没有从主题的第一原则制定，那么这样的模型可能有一个弱相关性的实际问题。
[[File:Logical deterministic individual-based cellular automata model of interspecific competition for a single limited resource.gif|thumb|Logical deterministic individual-based cellular automata model of interspecific competition for a single limited resource]]
Logical deterministic individual-based cellular automata model of interspecific competition for a single limited resource
对于单个有限资源，基于逻辑确定性个体的种间竞争元胞自动机模型
==Hardware-based ("hard")==
==Hardware-based ("hard")==
基于硬件(“硬件”)
{{Further|Robot}}
Hardware-based artificial life mainly consist of ''robots'', that is, [[automation|automatically]] guided [[machine]]s able to do tasks on their own.
Hardware-based artificial life mainly consist of robots, that is, automatically guided machines able to do tasks on their own.
基于硬件的人工生命主要由机器人组成，即能够自动引导机器完成任务的机器人。
==Biochemical-based ("wet")==
==Biochemical-based ("wet")==
生物化学为基础(“湿”)
{{Further|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 (brain)|wetware]]".
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".
生物化学为基础的生命是在合成生物学领域研究。它涉及到。合成 DNA 的创造。“湿”一词是“湿件”一词的延伸。
In May 2019, researchers, in a milestone effort, reported the creation of a new [[Synthetic biology#Synthetic life|synthetic]] (possibly [[Artificial life#Biochemical-based ("wet")|artificial]]) form of [[wikt:viability|viable]] [[life]], a variant of the [[bacteria]] ''[[Escherichia coli]]'', by reducing the natural number of 64 [[codon]]s in the bacterial [[genome]] to 59 codons instead, in order to encode 20 [[amino acid]]s.<ref name="NYT-20190515">{{cite news |last=Zimmer |first=Carl |authorlink=Carl Zimmer |title=Scientists Created Bacteria With a Synthetic Genome. Is This Artificial Life? - In a milestone for synthetic biology, colonies of E. coli thrive with DNA constructed from scratch by humans, not nature. |url=https://www.nytimes.com/2019/05/15/science/synthetic-genome-bacteria.html |date=15 May 2019 |work=[[The New York Times]] |accessdate=16 May 2019 }}</ref><ref name="NAT-20190515">{{cite journal |author=Fredens, Julius |display-authors=et al. |title=Total synthesis of Escherichia coli with a recoded genome |date=15 May 2019 |journal=[[Nature (journal)|Nature]] |doi=10.1038/s41586-019-1192-5 |pmid=31092918 |pmc=7039709 |volume=569 |issue=7757 |pages=514–518 }}</ref>
In May 2019, researchers, in a milestone effort, reported the creation of a new synthetic (possibly artificial) form of viable life, a variant of the bacteria Escherichia coli, by reducing the natural number of 64 codons in the bacterial genome to 59 codons instead, in order to encode 20 amino acids.
2019年5月，研究人员在一项具有里程碑意义的工作中报告了一种新的合成的(可能是人工的)有生命的生命形式的创造，它是大肠桿菌的变种，通过将细菌基因组中64个密码子的自然数目减少到59个密码子来编码20个氨基酸。
== Open problems ==
== Open problems ==
开放性问题
;How does life arise from the nonliving?<ref>{{cite web|url=http://libarynth.org/open_problems_in_alife|title=Libarynth|accessdate=2015-05-11}}</ref><ref>{{cite web|url=http://authors.library.caltech.edu/13564/1/BEDal00.pdf|title=Caltech |accessdate=2015-05-11}}</ref>
How does life arise from the nonliving?
生命是如何从无生命中产生的？
*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?
What are the potentials and limits of living systems?
生命系统的潜力和限制是什么？
*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 (information theory)|information flow]], and information generation for evolving systems.
;How is life related to mind, machines, and culture?
How is life related to mind, machines, and culture?
生命是如何与思想、机器和文化联系起来的？
*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 ==
== Related subjects ==
相关科目
#[[Artificial intelligence]] has traditionally used a [[Top-down and bottom-up design|top down]] approach, while alife generally works from the bottom up.<ref>{{cite web |url=http://lggwg.com/wolff/aicg99/stern.html |title=AI Beyond Computer Games |accessdate=2008-07-04 |archiveurl=https://web.archive.org/web/20080701040911/http://www.lggwg.com/wolff/aicg99/stern.html |archivedate=2008-07-01 |url-status=dead }}</ref>
Artificial intelligence has traditionally used a top down approach, while alife generally works from the bottom up.
人工智能传统上使用自上而下的方法，而生活通常是自下而上的。
#[[Artificial chemistry]] started as a method within the alife community to abstract the processes of chemical reactions.
Artificial chemistry started as a method within the alife community to abstract the processes of chemical reactions.
人工化学最初是作为生命群体中抽象化学反应过程的一种方法而出现的。
#[[Evolutionary algorithm]]s are a practical application of the weak alife principle applied to [[optimization problem]]s. 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|date=August 2007}} The following is a list of evolutionary algorithms closely related to and used in alife:
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. The following is a list of evolutionary algorithms closely related to and used in alife:
进化算法是弱生命原理在优化问题中的实际应用。许多优化算法是借鉴或模仿现实生活技术而精心设计的。主要的区别在于明确定义一个代理人的适合性，根据其解决问题的能力，而不是它的能力，找到食物，繁殖，或避免死亡。以下是一系列与生活密切相关并用于生活的进化算法:
#*[[Ant colony optimization]]
*Ant colony optimization
* 蚁群优化
#*[[Bacterial colony optimization]]
*Bacterial colony optimization
* 细菌菌落优化
#*[[Genetic algorithm]]
*Genetic algorithm
* 遗传算法
#*[[Genetic programming]]
*Genetic programming
* 遗传程式设计
#*[[Swarm intelligence]]
*Swarm intelligence
* 群体智能
#[[Multi-agent system]] – A multi-agent system is a computerized system composed of multiple interacting intelligent agents within an environment.
Multi-agent system – A multi-agent system is a computerized system composed of multiple interacting intelligent agents within an environment.
多智能体系统-多智能体系统是一个计算机化的系统，由一个环境中多个相互作用的智能代理组成。
#[[Evolutionary art]] uses techniques and methods from artificial life to create new forms of art.
Evolutionary art uses techniques and methods from artificial life to create new forms of art.
进化艺术使用人工生命的技术和方法来创造新的艺术形式。
#[[Evolutionary music]] uses similar techniques, but applied to music instead of visual art.
Evolutionary music uses similar techniques, but applied to music instead of visual art.
进化的音乐使用了类似的技术，但是应用于音乐而不是视觉艺术。
#[[Abiogenesis]] and the origin of life sometimes employ alife [[methodology|methodologies]] as well.
Abiogenesis and the origin of life sometimes employ alife methodologies as well.
自然发生和生命起源有时也使用生命学方法。
== History ==
== History ==
历史
{{Main|History of artificial life}}
== Criticism ==
== Criticism ==
批评
Alife has had a controversial history. [[John Maynard Smith]] criticized certain artificial life work in 1994 as "fact-free science".<ref>Horgan, J. 1995. [http://www2.econ.iastate.edu/tesfatsi/hogan.complexperplex.htm From Complexity to Perplexity]. Scientific American. p107</ref>
Alife has had a controversial history. John Maynard Smith criticized certain artificial life work in 1994 as "fact-free science".
阿里夫有一段颇具争议的历史。约翰·梅纳德·史密斯在1994年批评某些人工生命工作是“无事实的科学”。
== See also ==
== See also ==
参见
{{Portal|Systems science}}
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*{{annotated link|Artificial consciousness}}
*{{annotated link|Applications of artificial intelligence}}
*{{annotated link|Autonomous robot}}
*{{annotated link|Bioethics}}
*{{annotated link|Complex adaptive system}}
*{{annotated link|Darwin machine}}
*{{annotated link|Digital morphogenesis}}
*{{annotated link|Emergence}}
*{{annotated link|Life simulation game}}
*{{annotated link|List of emerging technologies}}
*{{annotated link|Mathematical and theoretical biology}}
*{{annotated link|Multi-agent system}}
*{{annotated link|Outline of artificial intelligence}}
*{{annotated link|Player Project}}
*{{annotated link|Simulated reality}}
*{{annotated link|Social simulation}}
*{{annotated link|Soda Constructor}}
*{{annotated link|Swarm intelligence}}
*{{annotated link|Synthetic life}}
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==References==
==References==
参考资料
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See http://en.wikipedia.org/wiki/Wikipedia:Footnotes for a
See http://en.wikipedia.org/wiki/Wikipedia:Footnotes for a
参见《 http://en.wikipedia.org/wiki/wikipedia:footnotes
discussion of different citation methods and how to generate
discussion of different citation methods and how to generate
讨论不同的引用方法和如何产生
footnotes using the <ref>, </ref> and <reference /> tags
footnotes using the and tags
使用 and 标签的脚注
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==External links==
==External links==
外部链接
*{{DMOZ|Computers/Artificial_Life}}
*[http://alife.org/ International Society of Artificial Life]
*[http://www.mitpressjournals.org/loi/artl ''Artificial Life''] journal, at MIT Press Journal
*[http://www.envirtech.com/artificial-life-lab.html The Artificial Life Lab], a virtual environment lab
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[[Category:Artificial life| ]]
[[Category:Scientific modeling]]
Category:Scientific modeling
类别: 科学建模
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<small>This page was moved from [[wikipedia:en:Artificial life]]. Its edit history can be viewed at [[人工生命/edithistory]]</small></noinclude>
[[Category:待整理页面]]