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The idea of human artifacts being given life has fascinated humankind for as long as people have been recording their myths and stories.  Whether Pygmalion or Frankenstein, humanity has been fascinated with the idea of artificial life.

The idea of human artifacts being given life has fascinated humankind for as long as people have been recording their myths and stories.  Whether Pygmalion or Frankenstein, humanity has been fascinated with the idea of artificial life.

== Pre-computer ==

== Pre-computer ==

 

Automatons were quite a novelty.  In the days before computers and electronics, some were very sophisticated, using pneumatics, mechanics, and hydraulics. The first automata were conceived during the third and second centuries BC and these were demonstrated by the theorems of Hero of Alexandria, which included sophisticated mechanical and hydraulic solutions. Many of his notable works were included in the book Pneumatics, which was also used for constructing machines until early modern times. In 1490, Leonardo da Vinci also constructed an armored knight, which is considered the first humanoid robot in Western civilization.

Automatons were quite a novelty.  In the days before computers and electronics, some were very sophisticated, using pneumatics, mechanics, and hydraulics. The first automata were conceived during the third and second centuries BC and these were demonstrated by the theorems of Hero of Alexandria, which included sophisticated mechanical and hydraulic solutions. Many of his notable works were included in the book Pneumatics, which was also used for constructing machines until early modern times. In 1490, Leonardo da Vinci also constructed an armored knight, which is considered the first humanoid robot in Western civilization.

Other early famous examples include al-Jazari's humanoid robots. This Arabic inventor once constructed a band of automata, which can be commanded to play different pieces of music. There is also the case of Jacques de Vaucanson's artificial duck exhibited in 1735, which had thousands of moving parts and one of the first to mimic a biological system. The duck could reportedly eat and digest, drink, quack, and splash in a pool.  It was exhibited all over Europe until it fell into disrepair.

Other early famous examples include al-Jazari's humanoid robots. This Arabic inventor once constructed a band of automata, which can be commanded to play different pieces of music. There is also the case of Jacques de Vaucanson's artificial duck exhibited in 1735, which had thousands of moving parts and one of the first to mimic a biological system. The duck could reportedly eat and digest, drink, quack, and splash in a pool.  It was exhibited all over Europe until it fell into disrepair.

其他早期著名的例子包括 al-Jazari 的人形机器人。这位阿拉伯发明家曾经建造了一台自动机，可以命令它演奏不同的音乐。还有1735年雅克 · 德 · 沃坎森的人造鸭展览，它有数千个活动部件，是第一个模拟生物系统的人造鸭。据报道，鸭子可以吃、消化、喝、呱呱叫，还可以在池塘里溅水。它在欧洲各地展出，直到年久失修。

其他早期著名的例子包括 al-Jazari 的人形机器人。这位阿拉伯发明家曾经构造了一组自动机，可以命令它们演奏不同的乐曲。还有1735年展出的雅克·德·沃康森的人造鸭子，它有数千个活动部件，是最早模仿生物系统的机器之一。据报道，这只鸭子能吃、能消化、能喝水、能嘎嘎叫，还能在游泳池里溅水。它在整个欧洲展出，直至其年久失修。

However, it wasn't until the invention of cheap computing power that artificial life as a legitimate science began in earnest, steeped more in the theoretical and computational than the mechanical and mythological.

However, it wasn't until the invention of cheap computing power that artificial life as a legitimate science began in earnest, steeped more in the theoretical and computational than the mechanical and mythological.

 

==1950s–1970s==

==1950s–1970s==

 

One of the earliest thinkers of the modern age to postulate the potentials of artificial life, separate from [[artificial intelligence]], was math and computer prodigy [[John von Neumann]].  At the [[Hixon Symposium]], hosted by [[Linus Pauling]] in [[Pasadena, California]] in the late 1940s, von Neumann delivered a lecture titled "The General and Logical Theory of Automata."  He defined an "automaton" as any machine whose behavior proceeded logically from step to step by combining information from the environment and its own programming, and said that natural organisms would in the end be found to follow similar simple rules.  He also spoke about the idea of [[self-replicating machine]]s.  He postulated a machine – a [[kinematic automaton]] – made up of a control computer, a construction arm, and a long series of instructions, floating in a lake of parts.  By following the instructions that were part of its own body, it could create an identical machine.  He followed this idea by creating (with [[Stanislaw Ulam]]) a purely logic-based automaton, not requiring a physical body but based on the changing states of the cells in an infinite grid – the first [[cellular automaton]].  It was extraordinarily complicated compared to later CAs, having hundreds of thousands of cells which could each exist in one of twenty-nine states, but von Neumann felt he needed the complexity in order for it to function not just as a self-replicating "machine", but also as a [[universal computer]] as defined by [[Alan Turing]].  This "[[Von Neumann universal constructor|universal constructor]]" read from a tape of instructions and wrote out a series of cells that could then be made active to leave a fully functional copy of the original machine and its tape. Von Neumann worked on his [[automata theory]] intensively right up to his death, and considered it his most important work.

One of the earliest thinkers of the modern age to postulate the potentials of artificial life, separate from [[artificial intelligence]], was math and computer prodigy [[John von Neumann]].  At the [[Hixon Symposium]], hosted by [[Linus Pauling]] in [[Pasadena, California]] in the late 1940s, von Neumann delivered a lecture titled "The General and Logical Theory of Automata."  He defined an "automaton" as any machine whose behavior proceeded logically from step to step by combining information from the environment and its own programming, and said that natural organisms would in the end be found to follow similar simple rules.  He also spoke about the idea of [[self-replicating machine]]s.  He postulated a machine – a [[kinematic automaton]] – made up of a control computer, a construction arm, and a long series of instructions, floating in a lake of parts.  By following the instructions that were part of its own body, it could create an identical machine.  He followed this idea by creating (with [[Stanislaw Ulam]]) a purely logic-based automaton, not requiring a physical body but based on the changing states of the cells in an infinite grid – the first [[cellular automaton]].  It was extraordinarily complicated compared to later CAs, having hundreds of thousands of cells which could each exist in one of twenty-nine states, but von Neumann felt he needed the complexity in order for it to function not just as a self-replicating "machine", but also as a [[universal computer]] as defined by [[Alan Turing]].  This "[[Von Neumann universal constructor|universal constructor]]" read from a tape of instructions and wrote out a series of cells that could then be made active to leave a fully functional copy of the original machine and its tape. Von Neumann worked on his [[automata theory]] intensively right up to his death, and considered it his most important work.

One of the earliest thinkers of the modern age to postulate the potentials of artificial life, separate from artificial intelligence, was math and computer prodigy John von Neumann.  At the Hixon Symposium, hosted by Linus Pauling in Pasadena, California in the late 1940s, von Neumann delivered a lecture titled "The General and Logical Theory of Automata."  He defined an "automaton" as any machine whose behavior proceeded logically from step to step by combining information from the environment and its own programming, and said that natural organisms would in the end be found to follow similar simple rules.  He also spoke about the idea of self-replicating machines.  He postulated a machine – a kinematic automaton – made up of a control computer, a construction arm, and a long series of instructions, floating in a lake of parts.  By following the instructions that were part of its own body, it could create an identical machine.  He followed this idea by creating (with Stanislaw Ulam) a purely logic-based automaton, not requiring a physical body but based on the changing states of the cells in an infinite grid – the first cellular automaton.  It was extraordinarily complicated compared to later CAs, having hundreds of thousands of cells which could each exist in one of twenty-nine states, but von Neumann felt he needed the complexity in order for it to function not just as a self-replicating "machine", but also as a universal computer as defined by Alan Turing.  This "universal constructor" read from a tape of instructions and wrote out a series of cells that could then be made active to leave a fully functional copy of the original machine and its tape. Von Neumann worked on his automata theory intensively right up to his death, and considered it his most important work.

One of the earliest thinkers of the modern age to postulate the potentials of artificial life, separate from artificial intelligence, was math and computer prodigy John von Neumann.  At the Hixon Symposium, hosted by Linus Pauling in Pasadena, California in the late 1940s, von Neumann delivered a lecture titled "The General and Logical Theory of Automata."  He defined an "automaton" as any machine whose behavior proceeded logically from step to step by combining information from the environment and its own programming, and said that natural organisms would in the end be found to follow similar simple rules.  He also spoke about the idea of self-replicating machines.  He postulated a machine – a kinematic automaton – made up of a control computer, a construction arm, and a long series of instructions, floating in a lake of parts.  By following the instructions that were part of its own body, it could create an identical machine.  He followed this idea by creating (with Stanislaw Ulam) a purely logic-based automaton, not requiring a physical body but based on the changing states of the cells in an infinite grid – the first cellular automaton.  It was extraordinarily complicated compared to later CAs, having hundreds of thousands of cells which could each exist in one of twenty-nine states, but von Neumann felt he needed the complexity in order for it to function not just as a self-replicating "machine", but also as a universal computer as defined by Alan Turing.  This "universal constructor" read from a tape of instructions and wrote out a series of cells that could then be made active to leave a fully functional copy of the original machine and its tape. Von Neumann worked on his automata theory intensively right up to his death, and considered it his most important work.

现代最早的思想家之一，假定人工生命的潜力，从人工智能分离出来，是数学和计算机神童约翰·冯·诺伊曼。上世纪40年代末，在加利福尼亚州帕萨迪纳市由莱纳斯 · 鲍林主持的 Hixon 研讨会上，冯 · 诺依曼发表了题为“自动机的一般和逻辑理论”的演讲他将“自动机”定义为任何一种机器，它的行为通过将来自环境的信息和自身的编程结合起来，从逻辑上一步一步地进行，并说自然界的有机体最终会被发现遵循类似的简单规则。他还谈到了自我复制机器的想法。他假设了一个机器——一个运动学自动机——由一个控制计算机、一个结构臂和一系列的指令组成，漂浮在零件的湖中。通过遵循它自己身体的一部分的指令，它可以创造一个完全相同的机器。他遵循这个想法，与 Stanislaw Ulam 一起创造了一个纯粹基于逻辑的自动机，不需要物理身体，而是基于无限网格中细胞状态的变化——第一个细胞自动机。与后来的 ca 相比，它非常复杂，拥有成千上万的细胞，每个细胞可以存在于29个状态中的一个，但冯 · 诺依曼觉得他需要这种复杂性，以便它不仅能够作为一台自我复制的“机器” ，而且能够作为一台由阿兰 · 图灵定义的通用计算机运行。这个“通用构造函数”读取指令磁带，并写出一系列单元格，这些单元格可以被激活，从而留下原始机器及其磁带的功能齐全的副本。冯 · 诺依曼一直致力于他的自动机理论，直到他去世，并认为这是他最重要的工作。

现代最早提出人工生命（独立于人工智能）潜力假说的思想家之一，是数学和计算机天才约翰·冯·诺依曼(John von Neumann)。20世纪40年代末，莱纳斯·鲍林（Linus Pauling）在加利福尼亚州帕萨迪纳市举办了希克森研讨会，冯·诺依曼在会上发表了题为“自动机的一般逻辑理论”的演讲。他将“自动机”定义为：通过结合环境信息和自身编程，可逻辑化地逐步执行行为动作的任何机器，并表示，最终人们会发现自然生物也遵循着类似的简单规则。他还谈到了自我复制机器的想法。他设想了一台机器——一台自动运动的机器——由一台控制计算机、一个构造臂和一长串指令组成，漂浮在零部件的湖中。通过执行它自己身体的一部分的指令，它就能制造出一台完全相同的机器。他遵循这个想法，创建了一个纯粹基于逻辑的自动机(与Stanislaw Ulam一起)，不需要物理实体，而是基于无限网格中细胞状态的变化——这是第一个细胞自动机（元胞自动机、格状自动机）。与后来的CAs相比，它是非常复杂的，它有成千上万的细胞，每个细胞可以存在于29个状态中的一个，但是冯·诺依曼觉得他需要这种复杂性，以便它不仅能作为一个自我复制的“机器”运行，而且能像艾伦·图灵定义的那样成为一台通用计算机。这个“通用构造函数”读取指令磁带，并写出一系列单元格，这些单元格可以被激活，从而留下原始机器及其磁带的功能齐全的副本。冯 · 诺依曼一直致力于他的自动机理论，直到他去世，并认为这是他最重要的工作。

Homer Jacobson illustrated basic self-replication in the 1950s with a model train set – a seed "organism" consisting of a "head" and "tail" boxcar could use the simple rules of the system to consistently create new "organisms" identical to itself, so long as there was a random pool of new boxcars to draw from.

Homer Jacobson illustrated basic self-replication in the 1950s with a model train set – a seed "organism" consisting of a "head" and "tail" boxcar could use the simple rules of the system to consistently create new "organisms" identical to itself, so long as there was a random pool of new boxcars to draw from.

[[Edward F. Moore]] proposed "Artificial Living Plants", which would be floating factories which could create copies of themselves.  They could be programmed to perform some function (extracting fresh water, harvesting minerals from seawater) for an investment that would be relatively small compared to the huge returns from the exponentially growing numbers of factories.  [[Freeman Dyson]] also studied the idea, envisioning self-replicating machines sent to explore and exploit other planets and moons, and a NASA group called the Self-Replicating Systems Concept Team performed a 1980 study on the feasibility of a self-building lunar factory.

[[Edward F. Moore]] proposed "Artificial Living Plants", which would be floating factories which could create copies of themselves.  They could be programmed to perform some function (extracting fresh water, harvesting minerals from seawater) for an investment that would be relatively small compared to the huge returns from the exponentially growing numbers of factories.  [[Freeman Dyson]] also studied the idea, envisioning self-replicating machines sent to explore and exploit other planets and moons, and a NASA group called the Self-Replicating Systems Concept Team performed a 1980 study on the feasibility of a self-building lunar factory.

Edward F. Moore proposed "Artificial Living Plants", which would be floating factories which could create copies of themselves.  They could be programmed to perform some function (extracting fresh water, harvesting minerals from seawater) for an investment that would be relatively small compared to the huge returns from the exponentially growing numbers of factories.  Freeman Dyson also studied the idea, envisioning self-replicating machines sent to explore and exploit other planets and moons, and a NASA group called the Self-Replicating Systems Concept Team performed a 1980 study on the feasibility of a self-building lunar factory.

Edward F. Moore proposed "Artificial Living Plants", which would be floating factories which could create copies of themselves.  They could be programmed to perform some function (extracting fresh water, harvesting minerals from seawater) for an investment that would be relatively small compared to the huge returns from the exponentially growing numbers of factories.  Freeman Dyson also studied the idea, envisioning self-replicating machines sent to explore and exploit other planets and moons, and a NASA group called the Self-Replicating Systems Concept Team performed a 1980 study on the feasibility of a self-building lunar factory.

爱德华 · f · 摩尔(edwardf.Moore)提出了“人造活植物”的概念，这种植物是漂浮的工厂，它们可以创造自己的复制品。可以对它们进行程序设计，让它们发挥某些功能(提取淡水、从海水中获取矿物质) ，相对于工厂数量呈指数增长所带来的巨大回报而言，这笔投资相对较小。弗里曼 · 戴森也研究了这个想法，设想了自我复制的机器被送去探索和开发其他行星和卫星，美国宇航局的一个团队称为自我复制系统概念小组在1980年进行了一项关于自我建造月球工厂可行性的研究。

爱德华·摩尔(Edward F. Moore)提出了“人造活植物”的概念，这种植物是漂浮的工厂，它们可以创造自己的复制品。可以对它们进行程序设计，让它们发挥某些功能(提取淡水，从海水中提取矿物质)，与指数级增长的工厂才能带来巨大的回报相比，这项投资的规模相对较小。弗里曼·戴森(Freeman Dyson)也研究了这个想法，设想了可自我复制的机器被送去探索和开发其他行星和卫星，美国宇航局(NASA)一个名为“自我复制系统概念小组”(self- replication Systems Concept Team)的团队在1980年进行了一项关于在月球上自行建造工厂的可行性研究。

University of Cambridge professor John Horton Conway invented the most famous cellular automaton in the 1960s.  He called it the Game of Life, and publicized it through Martin Gardner's column in Scientific American magazine.

University of Cambridge professor John Horton Conway invented the most famous cellular automaton in the 1960s.  He called it the Game of Life, and publicized it through Martin Gardner's column in Scientific American magazine.

==1970s–1980s==

==1970s–1980s==

 

Philosophy scholar [[Arthur Burks]], who had worked with von Neumann (and indeed, organized his papers after Neumann's death), headed the Logic of Computers Group at the [[University of Michigan]].  He brought the overlooked views of 19th century American thinker [[Charles Sanders Peirce]] into the modern age.  Peirce was a strong believer that all of nature's workings were based on logic (though not always deductive logic).  The Michigan group was one of the few groups still interested in alife and CAs in the early 1970s; one of its students, [[Tommaso Toffoli]] argued in his PhD thesis that the field was important because its results explain the simple rules that underlay complex effects in nature.  Toffoli later provided a key proof that CAs were [[Reversible computing|reversible]], just as the true universe is considered to be.

Philosophy scholar [[Arthur Burks]], who had worked with von Neumann (and indeed, organized his papers after Neumann's death), headed the Logic of Computers Group at the [[University of Michigan]].  He brought the overlooked views of 19th century American thinker [[Charles Sanders Peirce]] into the modern age.  Peirce was a strong believer that all of nature's workings were based on logic (though not always deductive logic).  The Michigan group was one of the few groups still interested in alife and CAs in the early 1970s; one of its students, [[Tommaso Toffoli]] argued in his PhD thesis that the field was important because its results explain the simple rules that underlay complex effects in nature.  Toffoli later provided a key proof that CAs were [[Reversible computing|reversible]], just as the true universe is considered to be.

Philosophy scholar Arthur Burks, who had worked with von Neumann (and indeed, organized his papers after Neumann's death), headed the Logic of Computers Group at the University of Michigan.  He brought the overlooked views of 19th century American thinker Charles Sanders Peirce into the modern age.  Peirce was a strong believer that all of nature's workings were based on logic (though not always deductive logic).  The Michigan group was one of the few groups still interested in alife and CAs in the early 1970s; one of its students, Tommaso Toffoli argued in his PhD thesis that the field was important because its results explain the simple rules that underlay complex effects in nature.  Toffoli later provided a key proof that CAs were reversible, just as the true universe is considered to be.

Philosophy scholar Arthur Burks, who had worked with von Neumann (and indeed, organized his papers after Neumann's death), headed the Logic of Computers Group at the University of Michigan.  He brought the overlooked views of 19th century American thinker Charles Sanders Peirce into the modern age.  Peirce was a strong believer that all of nature's workings were based on logic (though not always deductive logic).  The Michigan group was one of the few groups still interested in alife and CAs in the early 1970s; one of its students, Tommaso Toffoli argued in his PhD thesis that the field was important because its results explain the simple rules that underlay complex effects in nature.  Toffoli later provided a key proof that CAs were reversible, just as the true universe is considered to be.

哲学学者 Arthur Burks，曾与 von Neumann 共事(事实上，在 Neumann 死后组织了他的论文) ，领导了密歇根大学的计算机逻辑小组。他把19世纪美国思想家查尔斯·桑德斯·皮尔士的被忽视的观点带到了现代。皮尔斯坚信自然界所有的运作都是基于逻辑的(尽管并不总是演绎逻辑)。在20世纪70年代早期，密歇根大学的研究小组是少数几个仍对生命和复杂自然环境感兴趣的小组之一; 该小组的一名学生托马索 · 托福利在他的博士论文中指出，这个领域很重要，因为它的结果解释了自然界复杂效应背后的简单规则。托福利后来提供了一个关键的证据，证明了 ca 是可逆的，就像真正的宇宙被认为是可逆的一样。

曾与冯·诺依曼共事(事实上，在诺依曼去世后整理了他的论文)的著名学者亚瑟·伯克(Arthur Burks)领导了密歇根大学(University of Michigan)的“计算机逻辑小组”(Logic of Computers Group)。他把19世纪美国思想家查尔斯·桑德斯·皮尔斯（Charles Sanders Peirce）被忽视的观点带入现代。皮尔斯坚信自然界的一切活动都是基于逻辑的(尽管并不总是演绎逻辑)。

 

在20世纪70年代早期，密歇根大学的研究小组是少数几个仍然对生命和CAs感兴趣的研究小组之一;该小组的一名学生托马索·托福利(Tommaso Toffoli)在他的博士论文中指出，该领域非常重要，因为它的研究结果解释了自然界复杂效应背后的简单规则。托福利后来提供了一个关键的证据，证明CAs是可逆的，就像真正的宇宙被认为是可逆的一样。

Christopher Langton was an unconventional researcher, with an undistinguished academic career that led him to a job programming DEC mainframes for a hospital.  He became enthralled by Conway's Game of Life, and began pursuing the idea that the computer could emulate living creatures.  After years of study (and a near-fatal hang-gliding accident), he began attempting to actualize Von Neumann's CA and the work of Edgar F. Codd, who had simplified Von Neumann's original twenty-nine state monster to one with only eight states.  He succeeded in creating the first self-replicating computer organism in October 1979, using only an Apple II desktop computer.  He entered Burks' graduate program at the Logic of Computers Group in 1982, at the age of 33, and helped to found a new discipline.

Christopher Langton was an unconventional researcher, with an undistinguished academic career that led him to a job programming DEC mainframes for a hospital.  He became enthralled by Conway's Game of Life, and began pursuing the idea that the computer could emulate living creatures.  After years of study (and a near-fatal hang-gliding accident), he began attempting to actualize Von Neumann's CA and the work of Edgar F. Codd, who had simplified Von Neumann's original twenty-nine state monster to one with only eight states.  He succeeded in creating the first self-replicating computer organism in October 1979, using only an Apple II desktop computer.  He entered Burks' graduate program at the Logic of Computers Group in 1982, at the age of 33, and helped to found a new discipline.

克里斯托弗·兰顿是一位非传统的研究员，他平庸的学术生涯导致他在一家医院的 DEC 主机上编写程序。他被康威的《生命的游戏》迷住了，并开始追求计算机可以模仿生物的想法。经过多年的研究(以及一次几乎致命的悬挂滑翔事故) ，他开始尝试实现冯 · 诺依曼的 CA 和埃德加 · f · 科德的工作，后者将冯 · 诺依曼最初的29个状态的怪物简化为只有8个状态的怪物。1979年10月，他只用一台苹果 II 型台式电脑就成功地创造出了第一台可自我复制的电脑机体。1982年，33岁的他参加了伯克斯在计算机逻辑集团的研究生课程，并帮助建立了一个新的学科。

克里斯托弗·兰顿（Christopher Langton）是一位非传统的研究者，他平凡的学术生涯让他找到了一份为一家医院编程DEC大型机的工作。他被康威的“生命游戏”迷住了，并开始追求计算机可以模仿生物的想法。经过多年的研究(和一次几乎致命的悬挂式滑翔事故)，他开始尝试实现冯·诺依曼的CA和埃德加·科德（Edgar F. Codd）的工作，后者将冯·诺依曼最初的29个状态怪物简化为只有8个状态。1979年10月，他仅用一台Apple II型台式电脑就成功地创造出了第一台能够自我复制的计算机有机体。1982年，33岁的他加入了伯克在计算机逻辑小组的研究生课程，并帮助建立了一门新的学科。

Langton's official conference announcement of Artificial Life I was the earliest description of a field which had previously barely existed:

Langton's official conference announcement of Artificial Life I was the earliest description of a field which had previously barely existed:

兰顿在官方会议上宣布的《人工生命 i 》是对一个以前几乎不存在的领域的最早描述:

兰顿关于《人工生命》的官方会议公告是对这个之前几乎不存在的领域最早的描述:

<blockquote>Artificial life is the study of artificial systems that exhibit behavior characteristic of natural living systems.  It is the quest to explain life in any of its possible manifestations, without restriction to the particular examples that have evolved on earth.  This includes biological and chemical experiments, computer simulations, and purely theoretical endeavors.  Processes occurring on molecular, social, and evolutionary scales are subject to investigation.  The ultimate goal is to extract the logical form of living systems.</blockquote>

<blockquote>Artificial life is the study of artificial systems that exhibit behavior characteristic of natural living systems.  It is the quest to explain life in any of its possible manifestations, without restriction to the particular examples that have evolved on earth.  This includes biological and chemical experiments, computer simulations, and purely theoretical endeavors.  Processes occurring on molecular, social, and evolutionary scales are subject to investigation.  The ultimate goal is to extract the logical form of living systems.</blockquote>

<blockquote>Microelectronic technology and genetic engineering will soon give us the capability to create new life forms in silico as well as in vitro.  This capacity will present humanity with the most far-reaching technical, theoretical and ethical challenges it has ever confronted.  The time seems appropriate for a gathering of those involved in attempts to simulate or synthesize aspects of living systems.</blockquote>

<blockquote>Microelectronic technology and genetic engineering will soon give us the capability to create new life forms in silico as well as in vitro.  This capacity will present humanity with the most far-reaching technical, theoretical and ethical challenges it has ever confronted.  The time seems appropriate for a gathering of those involved in attempts to simulate or synthesize aspects of living systems.</blockquote>

Ed Fredkin founded the Information Mechanics Group at MIT, which united Toffoli, Norman Margolus, Gerard Vichniac, and Charles Bennett.  This group created a computer especially designed to execute cellular automata, eventually reducing it to the size of a single circuit board.  This "cellular automata machine" allowed an explosion of alife research among scientists who could not otherwise afford sophisticated computers.

Ed Fredkin founded the Information Mechanics Group at MIT, which united Toffoli, Norman Margolus, Gerard Vichniac, and Charles Bennett.  This group created a computer especially designed to execute cellular automata, eventually reducing it to the size of a single circuit board.  This "cellular automata machine" allowed an explosion of alife research among scientists who could not otherwise afford sophisticated computers.

In 1982, computer scientist named Stephen Wolfram turned his attention to cellular automata.  He explored and categorized the types of complexity displayed by one-dimensional CAs, and showed how they applied to natural phenomena such as the patterns of seashells and the nature of plant growth.

In 1982, computer scientist named Stephen Wolfram turned his attention to cellular automata.  He explored and categorized the types of complexity displayed by one-dimensional CAs, and showed how they applied to natural phenomena such as the patterns of seashells and the nature of plant growth.

[[Norman Packard]], who worked with Wolfram at the [[Institute for Advanced Study]], used CAs to simulate the growth of snowflakes, following very basic rules.

[[Norman Packard]], who worked with Wolfram at the [[Institute for Advanced Study]], used CAs to simulate the growth of snowflakes, following very basic rules.

Norman Packard, who worked with Wolfram at the Institute for Advanced Study, used CAs to simulate the growth of snowflakes, following very basic rules.

Norman Packard, who worked with Wolfram at the Institute for Advanced Study, used CAs to simulate the growth of snowflakes, following very basic rules.

Computer animator Craig Reynolds similarly used three simple rules to create recognizable flocking behaviour in a computer program in 1987 to animate groups of boids.  With no top-down programming at all, the boids produced lifelike solutions to evading obstacles placed in their path.  Computer animation has continued to be a key commercial driver of alife research as the creators of movies attempt to find more realistic and inexpensive ways to animate natural forms such as plant life, animal movement, hair growth, and complicated organic textures.

Computer animator Craig Reynolds similarly used three simple rules to create recognizable flocking behaviour in a computer program in 1987 to animate groups of boids.  With no top-down programming at all, the boids produced lifelike solutions to evading obstacles placed in their path.  Computer animation has continued to be a key commercial driver of alife research as the creators of movies attempt to find more realistic and inexpensive ways to animate natural forms such as plant life, animal movement, hair growth, and complicated organic textures.

J. Doyne Farmer was a key figure in tying artificial life research to the emerging field of complex adaptive systems, working at the Center for Nonlinear Studies (a basic research section of Los Alamos National Laboratory), just as its star chaos theorist Mitchell Feigenbaum was leaving.  Farmer and Norman Packard chaired a conference in May 1985 called "Evolution, Games, and Learning", which was to presage many of the topics of later alife conferences.

J. Doyne Farmer was a key figure in tying artificial life research to the emerging field of complex adaptive systems, working at the Center for Nonlinear Studies (a basic research section of Los Alamos National Laboratory), just as its star chaos theorist Mitchell Feigenbaum was leaving.  Farmer and Norman Packard chaired a conference in May 1985 called "Evolution, Games, and Learning", which was to presage many of the topics of later alife conferences.

是将人工生命研究与复杂适应系统这一新兴领域联系起来的关键人物，他在非线性研究中心(洛斯阿拉莫斯国家实验室的基础研究部门)工作，就在其明星混沌理论家米切尔·费根鲍姆离开的时候。1985年5月，法默和诺曼 · 帕卡德主持了一次名为“进化、游戏和学习”的会议，这次会议预示了后来人生会议的许多主题。

多恩·法默（J. Doyne Farmer）是将人工生命研究与复杂自适应系统这一新兴领域联系起来的关键人物，他在非线性研究中心(洛斯阿拉莫斯国家实验室的一个基础研究部门)工作，就在其明星混沌理论学家米切尔·费根鲍姆即将离开的时候。1985年5月，法默和诺曼·帕卡德（Norman Packard）主持了一个名为“进化、游戏和学习”的会议，这预示了后来的人工生命会议的许多主题。

==2000s==

==2000s==

On the ecological front, research regarding the evolution of animal cooperative behavior (started by [[W. D. Hamilton]] in the 1960s <ref>Hamilton, W. D. The genetical evolution of social behaviour. I and II. J. Theor.

On the ecological front, research regarding the evolution of animal cooperative behavior (started by [[W. D. Hamilton]] in the 1960s <ref>Hamilton, W. D. The genetical evolution of social behaviour. I and II. J. Theor.

On the ecological front, research regarding the evolution of animal cooperative behavior (started by W. D. Hamilton in the 1960s <ref>Hamilton, W. D. The genetical evolution of social behaviour. I and II. J. Theor.

On the ecological front, research regarding the evolution of animal cooperative behavior (started by W. D. Hamilton in the 1960s <ref>Hamilton, W. D. The genetical evolution of social behaviour. I and II. J. Theor.

在生态学方面，对动物合作行为进化的研究(由汉密尔顿于20世纪60年代开始，文献记载为汉密尔顿。 社会行为的遗传进化。I 及 II。J. Theor.

在生态方面，2006年，彼得·图尔钦（Peter Turchin）和米哈伊尔·伯切夫（Mikhail Burtsev）通过人工生命重新引入了关于动物合作行为进化的研究(由上世纪60年代的汉密尔顿（W. D. Hamilton）发起，产生了亲缘选择、互惠、多层次选择和文化群体选择等理论)。

Biol. 7, 1–52 (1964).</ref><ref>Axelrod, R. & Hamilton, W. D. The evolution of cooperation. Science 211,

Biol. 7, 1–52 (1964).</ref><ref>Axelrod, R. & Hamilton, W. D. The evolution of cooperation. Science 211,

1390–1396 (1981).</ref> resulting in theories of kin selection, reciprocity, multilevel selection and cultural group selection) was re-introduced via artificial life by Peter Turchin and Mikhail Burtsev in 2006. Previously, game theory has been utilized in similar investigation, however, that approach was deemed to be rather limiting in its amount of possible strategies and debatable set of payoff rules. The alife model designed here, instead, is based upon Conway's Game of Life but with much added complexity (there are over 10¹⁰⁰⁰ strategies that can potentially emerge). Most significantly, the interacting agents are characterized by external phenotype markers which allows for recognition amongst in-group members. In effect, it is shown that given the capacity to perceive these markers, agents within the system are then able to evolve new group behaviors under minimalistic assumptions. On top of the already known strategies of the bourgeois-hawk-dove game, here two novel modes of cooperative attack and defense arise from the simulation.

1390–1396 (1981).</ref> resulting in theories of kin selection, reciprocity, multilevel selection and cultural group selection) was re-introduced via artificial life by Peter Turchin and Mikhail Burtsev in 2006. Previously, game theory has been utilized in similar investigation, however, that approach was deemed to be rather limiting in its amount of possible strategies and debatable set of payoff rules. The alife model designed here, instead, is based upon Conway's Game of Life but with much added complexity (there are over 10¹⁰⁰⁰ strategies that can potentially emerge). Most significantly, the interacting agents are characterized by external phenotype markers which allows for recognition amongst in-group members. In effect, it is shown that given the capacity to perceive these markers, agents within the system are then able to evolve new group behaviors under minimalistic assumptions. On top of the already known strategies of the bourgeois-hawk-dove game, here two novel modes of cooperative attack and defense arise from the simulation.

1390-1396(1981). 2006年，Peter Turchin 和 Mikhail Burtsev 通过人工生命重新引入了亲缘选择、互惠、多层选择和文化群选择等理论。在此之前，博弈论被用于类似的研究，但是这种方法被认为在可能策略的数量和有争议的支付规则集方面是相当有限的。这里设计的生命模型是基于 Conway 的生命游戏，但是增加了很多复杂性(有超过10个 sup 1000 / sup 策略可能出现)。最重要的是，相互作用的代理人是拥有属性的外部表型标记，它允许在组内成员之间的识别。实际上，研究表明，给予感知这些标记的能力，系统内的代理人就能够在最简假设下进化出新的群体行为。在已知的资产阶级-鹰派-鸽派对策策略之上，这里有两种新颖的合作攻击和防御模式从模拟中产生。

在此之前，博弈论被用于类似的研究，然而，该方法被认为是数量相当有限的可能策略和有争议的支付规则集。相反，这里设计的人工生命模型是基于康威的生命游戏，但增加了很多复杂性(可能会出现超过10¹⁰⁰⁰种策略)。最重要的是，相互作用的因子具有外部表型标记，可在组内成员之间识别。实际上，它表明，如果有能力感知这些标记，系统内的因子就能够在最简假设下进化出新的群体行为。在已知的资产阶级-鹰派-鸽派对策策略之上，这里有两种新颖的合作攻击和防御模式从模拟中产生。

For the setup, this two-dimensional artificial world is divided into cells, each empty or containing a resource bundle. An empty cell can acquire a resource bundle with a certain probability per unit of time and lose it when an agent consumes the resource. Each agent is plainly constructed with a set of receptors, effectors (the components that govern the agents' behavior), and neural net which connect the two. In response to the environment, an agent may rest, eat, reproduce by division, move, turn and attack. All actions <!--is this true? (see next comment)--> expend energy taken from its internal energy storage; once that is depleted, the agent dies. Consumption of resource, as well as other agents after defeating them, yields an increase <!--or should this say "a net increase"?--> in the energy storage. Reproduction is modeled as being asexual while the offspring receive half the parental energy. Agents are also equipped with sensory inputs that allow them to detect resources or other members within a parameter in addition to its own level of vitality. As for the phenotype markers, they do not influence behavior but solely function as indicator of 'genetic' similarity. Heredity is achieved by having the relevant information be inherited by the offspring and subjected to a set rate of mutation.

For the setup, this two-dimensional artificial world is divided into cells, each empty or containing a resource bundle. An empty cell can acquire a resource bundle with a certain probability per unit of time and lose it when an agent consumes the resource. Each agent is plainly constructed with a set of receptors, effectors (the components that govern the agents' behavior), and neural net which connect the two. In response to the environment, an agent may rest, eat, reproduce by division, move, turn and attack. All actions <!--is this true? (see next comment)--> expend energy taken from its internal energy storage; once that is depleted, the agent dies. Consumption of resource, as well as other agents after defeating them, yields an increase <!--or should this say "a net increase"?--> in the energy storage. Reproduction is modeled as being asexual while the offspring receive half the parental energy. Agents are also equipped with sensory inputs that allow them to detect resources or other members within a parameter in addition to its own level of vitality. As for the phenotype markers, they do not influence behavior but solely function as indicator of 'genetic' similarity. Heredity is achieved by having the relevant information be inherited by the offspring and subjected to a set rate of mutation.

对于这个设置，这个二维的人工世界被划分为个单元，每个单元为空或包含一个资源包。一个空单元可以获得单位时间内一定概率的资源包，并在代理消耗该资源时丢失它。每个代理明显构建了一套受体，效应器(控制代理的行为的组成部分)和神经网络连接两者。作为对环境的反应，代理人可以通过分裂休息、进食、繁殖、移动、转向和攻击。所有的行动! ——这是真的吗？(见下一条评论)——从其内部能量储存中消耗能量; 一旦耗尽，该物质就死亡。资源的消耗，以及在击败它们之后的其他代理人，产生能量储存的增加! ——或者应该说“净增加” 。生殖被认为是无性繁殖，而后代则接收到双亲一半的能量。代理还配备了感官输入，使他们能够发现资源或其他成员的参数除了自己的活力水平。至于表型标记，它们不影响行为，而只是作为“遗传”相似性的指标。遗传是通过让后代继承相关信息并经历一定的突变率来实现的。

对于该设置，这个二维人工世界被划分为单元，每个单元为空或包含一个资源包。一个空单元可以获得单位时间内一定概率的资源包，并在因子消耗该资源时丢失它。每个因子都是由一组受体、效应器(控制因子行为的组件)和连接两者的神经网络构成的。为了对环境做出反应，个体可以休息、进食、分裂繁殖、移动、转身和攻击。所有的动作消耗的能量来自于它的内部能量储存;一旦耗尽，因子就会死亡。消耗资源，以及击败其他因子后，产生能量储存的增加。繁殖模式为无性繁殖，其后代获得双亲能量的一半。因子还配备了感官输入，允许它们检测一个参数内除了它自己活力水平以外的资源或其他成员。至于表型标记，它们并不影响行为，而仅仅作为“遗传”相似性的指标。遗传是通过让后代继承相关的信息并承受一定的突变率来实现的。

 

The objective of the investigation is to study how the presence of phenotype markers affects the model's range of evolving cooperative strategies. In addition, as the resource available in this 2D environment is capped, the simulation also serves to determine the effect of environmental carrying capacity on their emergence.

The objective of the investigation is to study how the presence of phenotype markers affects the model's range of evolving cooperative strategies. In addition, as the resource available in this 2D environment is capped, the simulation also serves to determine the effect of environmental carrying capacity on their emergence.

 

One previously unseen strategy is termed the "raven". These agents leave cells with in-group members, thus avoiding intra-specific competition, and attack out-group members voluntarily. Another strategy, named the 'starling', involves the agent sharing cells with in-group members. Despite individuals having smaller energy storage due to resource partitioning, this strategy permits highly effective defense against large invaders via the advantage in numbers. Ecologically speaking, this resembles the mobbing behavior that characterizes many species of small birds when they collectively defend against the predator.

One previously unseen strategy is termed the "raven". These agents leave cells with in-group members, thus avoiding intra-specific competition, and attack out-group members voluntarily. Another strategy, named the 'starling', involves the agent sharing cells with in-group members. Despite individuals having smaller energy storage due to resource partitioning, this strategy permits highly effective defense against large invaders via the advantage in numbers. Ecologically speaking, this resembles the mobbing behavior that characterizes many species of small birds when they collectively defend against the predator.

In conclusion, the research claims that the simulated results have important implications for the evolution of territoriality by showing that within the alife framework it is possible to "model not only how one strategy displaces another, but also the very process by which new strategies emerge from a large quantity of possibilities".

In conclusion, the research claims that the simulated results have important implications for the evolution of territoriality by showing that within the alife framework it is possible to "model not only how one strategy displaces another, but also the very process by which new strategies emerge from a large quantity of possibilities".

 

Work is also underway to create cellular models of artificial life.  Initial work on building a complete biochemical model of cellular behavior is underway as part of a number of different research projects, namely Blue Gene which seeks to understand the mechanisms behind protein folding.

Work is also underway to create cellular models of artificial life.  Initial work on building a complete biochemical model of cellular behavior is underway as part of a number of different research projects, namely Blue Gene which seeks to understand the mechanisms behind protein folding.