人工生命

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25px  此article介紹的是a field of research。For artificially created life forms, see Synthetic life. For the journal, see Artificial Life (journal).

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.

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。

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

概览

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.

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

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".

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".

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 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. 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.

Software-based ("soft")

基于软件的(“软”)

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.

• Artificial 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

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”

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	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	| 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	| 可执行 DNA | | 2010 | | | 持续
TechnoSphere	modules	1995		TechnoSphere	modules	1995	1995年
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

基于程序的

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.

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

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

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

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

复杂的系统建模

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). 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> 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。

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")

基于硬件(“硬件”)

Hardware-based artificial life mainly consist of robots, that is, automatically guided machines 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")

生物化学为基础(“湿”)

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".

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 (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.

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

开放性问题

How does life arise from the nonliving?

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, 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

相关科目

1. Artificial intelligence has traditionally used a top down approach, while alife generally works from the bottom up.

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。

1. 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。

1. 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:

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:

1. • Ant colony optimization

• Ant colony optimization

• 蚁群优化

1. • Bacterial colony optimization

• Bacterial colony optimization

• 细菌菌落优化

1. • Genetic algorithm

• Genetic algorithm

• 遗传算法

1. • Genetic programming

• Genetic programming

• 遗传程式设计

1. • Swarm intelligence

• Swarm intelligence

• 群体智能

1. 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。

1. 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。

1. 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。

1. Abiogenesis and the origin of life sometimes employ alife 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

历史

Main article: History of artificial life

Criticism

批评

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

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

参见

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References

参考资料

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External links

外部链接

• 模板:DMOZ

• International Society of Artificial Life

• Artificial Life journal, at MIT Press Journal

• The Artificial Life Lab, a virtual environment lab

Category:Scientific modeling

类别: 科学建模

This page was moved from wikipedia:en:Artificial life. Its edit history can be viewed at 人工生命/edithistory