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.
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A 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 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-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")
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
This is a list of artificial life/digital organism simulators, organized by the method of creature definition.
| Name | Driven By | Started | Ended |
|---|---|---|---|
| ApeSDK (formerly Noble Ape) | language/social simulation | 1996 | ongoing |
| Avida | executable DNA | 1993 | ongoing |
| Biogenesis | executable DNA | 2006 | ongoing |
| Neurokernel | Geppetto | 2014 | ongoing |
| Creatures | neural net/simulated biochemistry | 1996-2001 | Fandom still active to this day, some abortive attempts at new products |
| Critterding | neural net | 2005 | ongoing |
| Darwinbots | executable DNA | 2003 | ongoing |
| DigiHive | executable DNA | 2006 | ongoing |
| DOSE | executable DNA | 2012 | ongoing |
| EcoSim | Fuzzy Cognitive Map | 2009 | ongoing |
| Framsticks | executable DNA | 1996 | ongoing |
| Geb | neural net | 1997 | ongoing |
| OpenWorm | Geppetto | 2011 | ongoing |
| Polyworld | neural net | 1990 | ongoing |
| Primordial Life | executable DNA | 1994 | 2003 |
| ScriptBots | executable DNA | 2010 | ongoing |
| TechnoSphere | modules | 1995 |
|
| Tierra | executable DNA | 1991 | 2004 |
| 3D Virtual Creature Evolution | neural net | 2008 | NA |
Program-based
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.
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 which 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, more on
learning 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). 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.
Logical deterministic individual-based cellular automata model of interspecific competition for a single limited resource
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 e.g. 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, 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.
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
- 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.
Criticism
Alife has had a controversial history. John Maynard Smith criticized certain artificial life work in 1994 as "fact-free science".
