View source for Artificial life

{{Short description|Field of study}}
'''Artificial life''' (often abbreviated '''ALife''' or '''A-Life''') is a field of study wherein researchers examine [[Philosophy:System|system]]s related to natural life, its processes, and its evolution, through the use of [[Simulation|simulation]]s with computer models, robotics, and [[Biology:Biochemistry|biochemistry]].<ref>{{cite web|url=http://dictionary.reference.com/browse/artificial%20life|title=Dictionary.com definition|access-date=2007-01-19}}</ref> The discipline was named by [[Biography:Christopher Langton|Christopher Langton]], an American theoretical biologist, in 1986.<ref>[https://books.google.com/books?id=-wt1aZrGXLYC&pg=PA37 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|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|access-date=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 [[Biography:Andrew Adamatzky|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|software]]; ''hard'',<ref>{{cite book |title=Artificial Life Models in Hardware|author=[[Biography:Andrew Adamatzky|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'', from biochemistry. Artificial life researchers study traditional [[Biology:Biology|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|access-date=2007-01-19 |archive-url=https://web.archive.org/web/20070117220840/http://zooland.alife.org/|archive-date=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>

[[File:Braitenberg vehicle (simulation made with breve).jpg|thumb|right|250px|A [[Braitenberg vehicle|Braitenberg vehicle]] simulation, programmed in breve, an artificial life simulator]]

== Overview ==
Artificial life studies the fundamental processes of living systems in artificial environments in order to gain a deeper understanding of the complex information processing that define such systems.  These topics are broad, but often include [[Evolutionary algorithm|evolutionary dynamics]], [[Philosophy:Emergence|emergent properties of collective systems]], [[Biology:Biomimetics|biomimicry]], as well as related issues about the [[Biology:Philosophy of biology|philosophy of the nature of life]] and the use of lifelike properties in artistic works.{{cn|date=April 2023}}

== Philosophy ==

The modeling philosophy of artificial life strongly differs from traditional modeling by studying not only "life-as-we-know-it" but also "life-as-it-might-be".<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>

A traditional model of a biological system will focus on capturing its most important parameters. In contrast, an alife modeling approach will generally seek to decipher the most simple and general principles underlying life and implement them in a simulation. The simulation then offers the possibility to analyse new and different lifelike systems.

Vladimir Georgievich Red'ko proposed to generalize this distinction to the modeling of any process, leading to the more general distinction of "processes-as-we-know-them" and "processes-as-they-could-be".<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>

At present, the commonly accepted definition of life does not consider any current alife simulations or [[Software|software]] to be alive, and they do not constitute part of the evolutionary process of any [[Earth:Ecosystem|ecosystem]]. However, different opinions about artificial life's potential have arisen:
* The ''strong alife'' (cf. [[Philosophy: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" ([[Biography:John von Neumann|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, [[Biography: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|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=en|quote=The intent of this work is to synthesize rather than simulate life.|archive-url=https://web.archive.org/web/20150711051305/http://life.ou.edu/pubs/alife2/tierra.tex|archive-date=2015-07-11|access-date=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")==

=== Techniques ===
* [[Cellular automaton|Cellular automata]] were used in the early days of artificial life, and are still often used for ease of [[Scalability|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|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 [[Biology:Baldwin effect|Baldwin effect]].
* [[Neuroevolution]]

====Program-based====
{{Further|Software:Programming game}}
Program-based simulations contain organisms with a "genome" language.  This language is more often in the form of a Turing complete 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|artificial intelligence]] and [[Multi-agent system|multi-agent system/program]].

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

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

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

===Complex systems modeling===

Mathematical models of complex systems are of three types: black-box (phenomenological), [[Software: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">
{{Citation
 | last1 = Kalmykov
 | first1 = Lev V.
 | last2 = Kalmykov
 | first2 = Vyacheslav L.
 | title = A Solution to the Biodiversity Paradox by Logical Deterministic Cellular Automata
 | journal = Acta Biotheoretica
| volume = 63
 | issue = 2
 | pages = 1–19
 | year = 2015
 | doi = 10.1007/s10441-015-9257-9
 | pmid = 25980478
 | s2cid = 2941481
 }}</ref><ref name="Kalmykov Lev V., Kalmykov Vyacheslav L. White-box model">{{Citation
 | last1 = Kalmykov
 | first1 = Lev V.
 | last2 = Kalmykov
 | first2 = Vyacheslav L.
 | title = A white-box model of S-shaped and double S-shaped single-species population growth
 | journal = PeerJ
 | volume = 3:e948
| pages = e948
 | year = 2015
 | doi = 10.7717/peerj.948
 | pmid = 26038717
 | pmc = 4451025
 | doi-access = free
 }}</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. Due to the non-transparent nature of the model, interactions of subsystems cannot be investigated. In contrast, a white-box model of a complex dynamic system has ‘transparent walls’ and directly shows underlying mechanisms. All events at the micro-, meso- and macro-levels of a dynamic system are directly visible at all stages of a white-box model's 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. 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 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|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" />

[[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]]

=== Notable simulators ===
This is a list of artificial life and [[Biology:Digital organism|digital organism]] simulators:

{| class="wikitable sortable mw-collapsible"
|+List of notable simulators
|-
! Name !! Driven By !! Started !! Ended
|-
| [[Software:Polyworld|Polyworld]] || neural net || 1990 || ongoing
|-
| [[Tierra (computer simulation)|Tierra]] || evolvable code || 1991 || 2004
|-
| [[Software:Avida|Avida]] || evolvable code || 1993 || ongoing
|-
| TechnoSphere || modules || 1995 ||
|-
| [[Software:Framsticks|Framsticks]] || evolvable code || 1996 || ongoing
|-
| Creatures || neural net and simulated biochemistry & genetics || 1996–2001 || Fandom still active to this day, some abortive attempts at new products{{Citation needed|date=January 2022}}
|-
| [[Software:Genepool swimbots|GenePool]] || evolvable code || 1997 || ongoing
|-
| Aevol<ref>[http://aevol.fr/] Aevol</ref>|| evolvable code, with steps that mimick the central dogma || 2006 || ongoing
|-
| [[Software:3D Virtual Creature Evolution|3D Virtual Creature Evolution]] || neural net || 2008 || NA
|-
| [[EcoSim]] || Fuzzy Cognitive Map || 2009 || ongoing
|-
| [[OpenWorm]] || Geppetto || 2011 || ongoing
|-
| The Bibites || neural net || 2015 || ongoing
|-
| [[Lenia]] || continuous cellular automata || 2019 || ongoing
|}

==Hardware-based ("hard")==
{{Further|Engineering:Robot}}
Hardware-based artificial life mainly consist of ''robots'', that is, [[Engineering:Automation|automatically]] guided machines able to do tasks on their own.

==Biochemical-based ("wet")==
{{Further|Biology:Artificial cell|Biology:Synthetic biology}}
Biochemical-based life is studied in the field of [[Biology:Synthetic biology|synthetic biology]]. It involves research such as the creation of synthetic DNA. The term "wet" is an extension of the term "[[Wetware (brain)|wetware]]". Efforts toward "wet" artificial life focus on engineering live minimal cells from living bacteria ''[[Mycoplasma laboratorium]]'' and in building non-living biochemical cell-like systems from scratch.

In May 2019, researchers reported a new milestone in the creation of a new [[Biology:Synthetic biology#Synthetic life|synthetic]] (possibly [[Artificial life#Biochemical-based ("wet")|artificial]]) form of viable life, a variant of the [[Biology:Bacteria|bacteria]] ''[[Biology:Escherichia coli|Escherichia coli]]'', by reducing the natural number of 64 codons in the bacterial [[Biology:Genome|genome]] to 59 codons instead, in order to encode 20 [[Chemistry:Amino acid|amino acid]]s.<ref name="NYT-20190515">{{cite news |last=Zimmer |first=Carl |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={{wipe|The New York Times}} |access-date=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 |doi=10.1038/s41586-019-1192-5 |pmid=31092918 |pmc=7039709 |volume=569 |issue=7757 |pages=514–518 |bibcode=2019Natur.569..514F }}</ref>

== Open problems ==
;How does life arise from the nonliving?<ref>{{cite web|url=http://libarynth.org/open_problems_in_alife|title=Libarynth|access-date=2015-05-11}}</ref><ref>{{cite web|url=http://authors.library.caltech.edu/13564/1/BEDal00.pdf|title=Caltech |access-date=2015-05-11}}</ref>
* Generate a molecular proto-organism [[In vitro|in vitro]].
* Achieve the transition to life in an [[Chemistry:Artificial chemistry|artificial chemistry]] [[Biology:In silico|in silico]].
* Determine whether fundamentally novel living organizations can exist.
* Simulate a [[Biology:Unicellular organism|unicellular organism]] over its entire life cycle.
* Explain how rules and symbols are generated from physical dynamics in living systems.

;What are the potentials and limits of living systems?
* Determine what is inevitable in the open-ended evolution of life.
* Determine minimal conditions for evolutionary transitions from specific to generic response systems.
* Create a formal framework for synthesizing dynamical hierarchies at all scales.
* Determine the predictability of evolutionary consequences of manipulating organisms and ecosystems.
* Develop a theory of [[Information processing|information processing]], [[Information flow (information theory)|information flow]], and information generation for evolving systems.

;How is life related to mind, machines, and culture?
* Demonstrate the emergence of intelligence and mind in an artificial living system.
* Evaluate the influence of machines on the next major evolutionary transition of life.
* Provide a quantitative model of the interplay between cultural and biological evolution.
* Establish ethical principles for artificial life.

== Related subjects ==
# Agent-based modeling is used in artificial life and other fields to explore [[Philosophy:Emergence|emergence]] in systems.
# [[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 |access-date=2008-07-04 |archive-url=https://web.archive.org/web/20080701040911/http://www.lggwg.com/wolff/aicg99/stern.html |archive-date=2008-07-01 |url-status=dead }}</ref>
# [[Chemistry:Artificial chemistry|Artificial chemistry]] started as a method within the alife community to abstract the processes of chemical reactions.
# [[Evolutionary algorithm]]s are a practical application of the weak alife principle applied to [[Optimization problem|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:
#* Ant colony optimization
#* [[Bacterial colony optimization]]
#* [[Genetic algorithm]]
#* [[Genetic programming]]
#* [[Swarm intelligence]]
# [[Multi-agent system]] – A multi-agent system is a computerized system composed of multiple interacting intelligent agents within an environment.
# [[Evolutionary art]] uses techniques and methods from artificial life to create new forms of art.
# [[Evolutionary music]] uses similar techniques, but applied to music instead of visual art.
# [[Biology:Abiogenesis|Abiogenesis]] and the origin of life sometimes employ alife [[Methodology|methodologies]] as well.
# [[Quantum artificial life]] applies quantum algorithms to artificial life systems.

== History ==
{{Main|History of artificial life}}

== Criticism ==
Alife has had a controversial history. [[Biography:John Maynard Smith|John Maynard Smith]] criticized certain artificial life work in 1994 as "fact-free science".<ref>{{cite web |last=Horgan |first=J. |date=1995 |url=https://www2.econ.iastate.edu/tesfatsi/hogan.complexperplex.htm |title=From Complexity to Perplexity |work=Scientific American |page=107 }}</ref>

== See also ==

{{cmn|colwidth=30em|
* {{annotated link|Artificial consciousness}}
* {{annotated link|Applications of artificial intelligence}}
* {{annotated link|Engineering:Autonomous robot}}
* {{annotated link|Philosophy:Bioethics}}
* {{annotated link|Complex adaptive system}}
* {{annotated link|Darwin machine}}
* {{annotated link|Software:Life simulation game}}
* [[Social:List of emerging technologies|List of emerging technologies]]
* {{annotated link|Biology:Mathematical and theoretical biology}}
* {{annotated link|Outline of artificial intelligence}}
* {{annotated link|Software:Player Project}}
* {{annotated link|Philosophy:Simulated reality}}
* {{annotated link|Social:Social simulation}}
* {{annotated link|Software:Soda Constructor|''Soda Constructor''}}
* {{annotated link|Biology:Universal Darwinism}}
* {{annotated link|Software:Virtual pet}}
* {{annotated link|Software:Webots}}
}}

==References==
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{{Reflist}}

==External links==
* {{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
* [https://web.archive.org/web/20161010024824/http://www.envirtech.com/artificial-life-lab.html The Artificial Life Lab], a virtual environment lab

{{Computer science}}


[[Category:Artificial life| ]]
[[Category:Simulation]]

{{Sourceattribution|Artificial life}}