Artificial life

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 defines 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.

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 could 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). This view is rooted in von Neumann's work on cellular automata and universal constructors, which demonstrated that self-reproduction could be achieved by logic-based machines regardless of their physical substrate. 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. A central goal in the philosophy and modeling of artificial life is achieving Open-Ended Evolution (OEE). This refers to the capacity of a system to continually produce novel, complex, and adaptive behaviors or entities without reaching a stable equilibrium or predefined end-point. Researchers argue that OEE is a hallmark of natural life that current artificial systems have yet to fully replicate.

Software-based ("soft")

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.
• Neuroevolution

Program-based

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 and 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), white-box (mechanistic, based on the first principles) and grey-box (mixtures of phenomenological and mechanistic models). | doi-access = free

Notable simulators

This is a list of artificial life and digital organism simulators:

Hardware-based ("hard")

Hardware-based artificial life mainly consist of robots, that is, automatically guided machines able to do tasks on their own.

Biochemical-based ("wet")

Biochemical-based life is studied in the field of synthetic biology. It involves research such as the creation of synthetic DNA. The term "wet" is an extension of the term "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 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 2020, Sam Kriegman and Douglas Blackiston reported the creation of a biological robot aided by artificial intelligence.

In 2021, the same team that developed Xenobots reported a further breakthrough: the first biological robots capable of kinematic self-replication. Unlike traditional biological reproduction (growth/birth), these synthetic organisms spontaneously collect loose cells in their environment to assemble new copies of themselves, a process previously seen only at the molecular level.

Open problems

;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 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 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

1. Agent-based modeling is used in artificial life and other fields to explore emergence in systems.
2. Artificial intelligence has traditionally used a top down approach, while alife generally works from the bottom up.
3. Artificial chemistry started as a method within the alife community to abstract the processes of chemical reactions.
4. 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:

• Ant colony optimization
• Bacterial colony optimization
• Genetic algorithm
• Genetic programming
• Swarm intelligence

1. Multi-agent system – A multi-agent system is a computerized system composed of multiple interacting intelligent agents within an environment.
2. Evolutionary art uses techniques and methods from artificial life to create new forms of art.
3. Evolutionary music uses similar techniques, but applied to music instead of visual art.
4. Abiogenesis and the origin of life sometimes employ alife methodologies as well.
5. Quantum artificial life applies quantum algorithms to artificial life systems.

History

Main article: History of artificial life

Criticism

Artificial life has had a controversial history. John Maynard Smith criticized certain artificial life work in 1994 as "fact-free science". Mario Bunge criticized the ideas of strong artificial life as part of his wider critique of computationalism. He wrote that proponents of strong alife are mistakenly erasing the distinction between a simulation and the process that is being simulated. He had no such objections to the weak alife program.

References

See http://en.wikipedia.org/wiki/Wikipedia:Footnotes for a discussion of different citation methods and how to generate footnotes using the and tags

References

1. "Dictionary.com definition".
2. Bedau, Mark A.. (2014). "The Cambridge Handbook of Artificial Intelligence". Cambridge University Press.
3. 978-0-262-73144-7
4. (November 1997). "The Game Industry's Dr. Frankenstein". [[Imagine Media]].
5. Bedau, Mark A.. (November 2003). "Artificial life: organization, adaptation and complexity from the bottom up". [[Trends in Cognitive Sciences]].
6. Maciej Komosinski and [[Andrew Adamatzky]]. (2009). "Artificial Life Models in Software". Springer.
7. [[Andrew Adamatzky]] and Maciej Komosinski. (2009). "Artificial Life Models in Hardware". Springer.
8. Langton, Christopher. "What is Artificial Life?".
9. 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
10. See Langton, C. G. 1992. [http://www.probelog.com/texts/Langton_al.pdf Artificial Life] {{webarchive. link. (March 11, 2007 . Addison-Wesley. ., section 1)
11. 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.)
12. Kemeny, J. G.. (1967-07-14). "Theory of Self-Reproducing Automata. John von Neumann. Edited by Arthur W. Burks. University of Illinois Press, Urbana, 1966. 408 pp., illus. $10". Science.
13. (1991). "An approach to the synthesis of life". Artificial Life II, Santa Fe Institute Studies in the Sciences of Complexity.
14. Sayama, Hiroki. (April 2011). "Seeking open-ended evolution in Swarm Chemistry". IEEE.
15. (April 2019). "Open-Ended Evolution and Open-Endedness: Editorial Introduction to the Open-Ended Evolution I Special Issue". Artificial Life.
16. Zimmer, Carl. (15 May 2019). "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.". [[The New York Times]].
17. Fredens, Julius. (15 May 2019). "Total synthesis of Escherichia coli with a recoded genome". [[Nature (journal).
18. (2020-04-03). "Meet the Xenobots, Virtual Creatures Brought to Life (Published 2020)".
19. Kriegman, Sam. (2021-11-29). "Kinematic self-replication in reconfigurable organisms". Proceedings of the National Academy of Sciences.
20. "Libarynth".
21. "Caltech".
22. "AI Beyond Computer Games".
23. Horgan, J.. (1995). "From Complexity to Perplexity". Scientific American.
24. Kaldis, Byron. (2019). "Mario Bunge: A Centenary Festschrift". Springer Nature Switzerland AG.
25. ,