Artificial life is a field of study in which researchers examine systems related to natural life, its processes and its evolution through the use of simulations with computational models, robotics and biochemistry.
The discipline was named by Christopher Langton, an American theoretical biologist, in 1986. There are three main classes of alife, named for their approaches: soft, software; hard, from the hardware; and humid, of biochemistry. Artificial life researchers study traditional biology by trying to recreate aspects of biological phenomena.
Artificial life studies the fundamental processes of living systems in artificial environments in order to obtain 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 issues related to the philosophy of the nature of life and the use of real properties in artistic works.
The modeling philosophy of artificial life is strongly differentiated from traditional modeling by studying not only “life as we know it”, but also “life as it is possible”.
A traditional model of a biological system will focus on capturing its most important parameters. On the contrary, an alternative modeling approach will generally seek to decipher the simplest and most general principles that underlie life and will implement them in a simulation.
The simulation offers the possibility of analyzing new and different real systems.
Vladimir Georgievich Red’ko proposed to generalize this distinction to the modeling of any process, which leads to the more general distinction of “processes as we know them” and “processes-as-they-might-be”.
Currently, the commonly accepted definition of life does not consider that there is any simulation or current software alive, and is not part of the evolutionary process of any ecosystem.
However, different opinions on the potential of artificial life have emerged:
The strong position of life states that “life is a process that can be abstracted from any particular medium”
Notably, Tom Ray stated that his Earth program does not simulate life on a computer but synthesizes it.
The weak position of life denies the possibility of generating a “living process” outside of a chemical solution. Its researchers try to simulate the processes of life to understand the underlying mechanics of biological phenomena.
Cellular automata were used in the early days of artificial life and are still frequently used to facilitate scalability and parallelization. Alife and the cellular automaton share a closely linked story.
Artificial neural networks are sometimes used to model the brain of an agent. Although traditionally it is more an artificial intelligence technique, neural networks can be important to simulate the population dynamics of organisms that can learn.
The symbiosis between learning and evolution is fundamental for the theories about the development of instincts in organisms with greater neurological complexity, as in, for example, the Baldwin effect.