Artificial life (often abbreviated ALife or A-Life) is a field of study and an associated art form which examine systems related to 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 computer scientist, in 1986. There are three main kinds of alife, named for their approaches: soft, from software; hard, from hardware; and wet, from biochemistry. Artificial life imitates traditional biology by trying to recreate some aspects of biological phenomena.
- 1 Overview
- 2 Philosophy
- 3 Organizations
- 4 Software-based - "soft"
- 5 Hardware-based - "hard"
- 6 Biochemical-based - "wet"
- 7 Open problems in ALife
- 8 Related subjects
- 9 History
- 10 Criticism
- 11 See also
- 12 References
- 13 External links
Artificial life studies the logic of living systems in artificial environments in order to gain a deeper understanding of the complex information processing that defines such systems.
Also sometimes included in the umbrella term "artificial life" are agent based systems which are used to study the emergent properties of societies of agents.
While life is, by definition, alive, artificial life is generally referred to as being confined to a digital environment and existence.
The modeling philosophy of all life strongly differs from traditional modeling by studying not only “life-as-we-know-it” but also “life-as-it-might-be”.
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" 
At present, the commonly accepted definition of life does not consider any current alife simulations or software to be alive, and they do not constitute part of the evolutionary process of any ecosystem. However, different opinions about artificial life's potential have arisen:
- The strong alife (cf. Strong AI) position states that "life is a process which can be abstracted away from any particular medium" (John von Neumann). Notably, Tom Ray declared that his program Tierra is not simulating life in a computer but synthesizing it.
- 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"
- Cellular automata were used in the early days of artificial life, and are still often used for ease of scalability and parallelization. Alife and cellular automata share a closely tied history.
- Neural networks are sometimes used to model the brain of an agent. Although traditionally more of an artificial intelligence technique, neural nets can be important for simulating population dynamics of organisms that can learn. The symbiosis between learning and evolution is central to theories about the development of instincts in organisms with higher neurological complexity, as in, for instance, the Baldwin effect.
This is a list of artificial life/digital organism simulators, organized by the method of creature definition.
|EcoSim||Fuzzy Cognitive Map||2009||ongoing|
|Evolve 4.0||executable dna||1996||ongoing|
|Noble Ape||neural net||1996||ongoing|
|Primordial Life||executable dna||1994||2003|
|3D Virtual Creature Evolution||neural net||2008||NA|
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.
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.
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.
These simulations have creatures that learn and grow using neural nets or a close derivative. Emphasis is often, although not always, more on learning than on natural selection.
Hardware-based - "hard"
Biochemical-based - "wet"
Open problems in ALife
- How does life arise from the nonliving?
- 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 lifecycle.
- Explain how rules and symbols are generated from physical dynamics in living systems.
- What are the potentials and limits of living systems?
- Determine what is inevitable in the open-ended evolution of life.
- Determine minimal conditions for evolutionary transitions from specific to generic response systems.
- Create a formal framework for synthesizing dynamical hierarchies at all scales.
- Determine the predictability of evolutionary consequences of manipulating organisms and ecosystems.
- Develop a theory of information processing, information flow, and information generation for evolving systems.
- How is life related to mind, machines, and culture?
- Demonstrate the emergence of intelligence and mind in an artificial living system.
- Evaluate the influence of machines on the next major evolutionary transition of life.
- Provide a quantitative model of the interplay between cultural and biological evolution.
- Establish ethical principles for artificial life.
- Artificial intelligence has traditionally used a top down approach, while alife generally works from the bottom up.
- Artificial chemistry started as a method within the alife community to abstract the processes of chemical reactions.
- 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:
- 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.
- Abiogenesis and the origin of life sometimes employ alife methodologies as well.
- Molecules and Thoughts Y Tarnopolsky - 2003 "Artificial Life (often abbreviated as Alife or A-life) is a small universe existing parallel to the much larger Artificial Intelligence. The origins of both areas were different."
- "Dictionary.com definition". Retrieved 2007-01-19.
- The MIT Encyclopedia of the Cognitive Sciences, The MIT Press, p.37. ISBN 978-0-262-73144-7
- Mark A. Bedau (November 2003). "Artificial life: organization, adaptation and complexity from the bottom up" (PDF). TRENDS in Cognitive Sciences. Retrieved 2007-01-19.
- Maciej Komosinski and Andrew Adamatzky (2009). Artificial Life Models in Software. New York: Springer. ISBN 978-1-84882-284-9.
- Andrew Adamatzky and Maciej Komosinski (2009). Artificial Life Models in Hardware. New York: Springer. ISBN 978-1-84882-529-1.
- Langton, Christopher. "What is Artificial Life?". Archived from the original on 17 January 2007. Retrieved 2007-01-19.
- See Langton, C. G. 1992. Artificial Life. Addison-Wesley. ., section 1
- See Red'ko, V. G. 1999. Mathematical Modeling of Evolution. in: F. Heylighen, C. Joslyn and V. Turchin (editors): Principia Cybernetica Web (Principia Cybernetica, Brussels). For the importance of ALife modeling from a cosmic perspective, see also Vidal, C. 2008.The Future of Scientific Simulations: from Artificial Life to Artificial Cosmogenesis. In Death And Anti-Death, ed. Charles Tandy, 6: Thirty Years After Kurt Gödel (1906-1978) p. 285-318. Ria University Press.)
- "Libarynth". Retrieved 2015-05-11.
- "Caltech" (PDF). Retrieved 2015-05-11.
- "AI Beyond Computer Games". Archived from the original on 2008-07-01. Retrieved 2008-07-04.
- Horgan, J. 1995. From Complexity to Perplexity. Scientific American. p107
- Artificial life at DMOZ
- Artificial Life Framework
- International Society of Artificial Life
- Artificial Life at MIT Press Journal
- The Artificial Life Lab at Envirtech Island, Second Life
- aDiatomea: an artificial life experiment using highly detailed 3d generated diatoms
- JSimLife: an artificial life environment using DNA and neural networks