Artificial Life


Artificial life (alife) is a field of study and an associated art form which examine systems related to life, its processes, and its evolution through simulations using 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 biological phenomena, such as sexual reproduction and response to stimuli. The term ‘artificial life’ is often used to specifically refer to soft alife.

Artificial life studies the logic of living systems in artificial environments. The goal is to come to an 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.

The modeling philosophy of alife strongly differs from traditional modeling, by studying not only ‘life-as-we-know-it,’ but also ‘life-as-it-might-be.’ In the first approach, 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 analyze new, different life-like systems. Ukrainian artist and scientist Kliment Red’ko proposed to generalize this distinction not just to the modeling of life, but to any process. This led 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 softwares 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 (similar to Strong AI) position states that ‘life is a process which can be abstracted away from any particular medium.’ 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.

Cellular automata were used in the early days of artificial life, and they are still often used for ease of scalability and parallelization. A cellular automaton is lattice of ‘cells,’ each of which can have any one of a finite number of states. The state of all cells in the lattice are updated simultaneously and the state of the entire lattice advances in discrete time steps. The state of each cell in the lattice is updated according to a local rule which may depend on the state of the cell and its neighbors at the previous time step. Alife and cellular automata share a closely tied history.

Neural networks are sometimes used to model the brain of an agent, they are  an artificial system (made of artificial neuron cells). 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: a mechanism for specific selection for general learning ability. Selected offspring would tend to have an increased capacity for learning new skills rather than being confined to genetically coded, relatively fixed abilities. In effect, it places emphasis on the fact that the sustained behavior of a species or group can shape the evolution of that species. Suppose a species is threatened by a new predator and there is a behavior that makes it more difficult for the predator to kill individuals of the species. Individuals who learn the behavior more quickly will obviously be at an advantage. As time goes on, the ability to learn the behavior will improve (by genetic selection), and at some point it will seem to be an instinct.

A programming game is a computer game where the player has no direct influence on the course of the game. Instead, a computer program or script is written in some domain-specific programming language in order to control the actions of the characters (usually robots, tanks or bacteria, which seek to destroy each other). Most programming games can be considered environments of digital organisms, related to artificial life simulations. They can contain organisms with a complex DNA language, usually Turing complete (meaning they could function in a Turing Machine, a system of rules, states and transitions rather than a real machine). This language is more often in the form of a computer program than actual biological DNA. Assembly derivatives are the most common languages used. Use of cellular automata is common but not required.

In module-based alife individual modules are added to a creature. These modules modify the creature’s behaviors and characteristics either directly, by hard coding into the simulation (e.g. leg type A increases speed and metabolism), or indirectly, through the emergent interactions between a creature’s modules (e.g. 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.

In parameter-based alife organisms are generally constructed with predefined 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.

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


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