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Artificial life

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Artificial life, often called ALife, is a scientific field focused on studying systems related to natural life. Researchers examine the processes and evolution of living things using computer simulations, robotics, and biochemistry. Unlike traditional biology, which focuses on "life as we know it," ALife explores "life as it could be." This approach seeks to find the simplest, most general principles that underlie life. By implementing these principles in artificial environments, scientists can analyze new and different lifelike systems.

Unevolved swimbots showing genetic diversity
Unevolved swimbots showing genetic diversity

There are three primary approaches to artificial life, categorized by the medium used. The first is "soft" artificial life, which relies on software and computer models. The second is "hard" artificial life, which uses hardware like robots that can perform tasks autonomously. The third is "wet" artificial life, which uses biochemistry to study life-like processes.

Braitenberg vehicle (simulation made with breve)
Braitenberg vehicle (simulation made with breve)

This third category is often called "wetware" because it involves the chemical building blocks of life.

Soft artificial life uses several different technical methods to simulate life. One common method is using cellular automata, which are systems that follow simple rules to create complex patterns. Researchers also use artificial neural networks to model how an agent might have a brain. These networks help simulate population dynamics in organisms that can learn. Another method is program-based simulation, where organisms possess a "genome" made of computer code. In these systems, an organism "lives" when its code is executed, and mutations occur through random changes in that code.

Researchers also use module-based and parameter-based techniques in software simulations. In module-based systems, individual parts are added to a creature to change its behavior. For example, a specific leg type might increase an organism's speed or its metabolism. Parameter-based simulations work differently by using fixed behaviors controlled by mutating numbers. Each number, or parameter, controls a specific aspect of the organism in a defined way. These methods allow scientists to observe how complex behaviors emerge from simple starting rules.

The history of ALife is tied to key figures and milestones. The discipline was named by American computer scientist Christopher Langton in 1986. In 1987, Langton organized the first official conference on the subject in Los Alamos, New Mexico. Later, researcher Tom Ray developed a program called Tierra. Ray famously claimed that Tierra was not merely simulating life, but was actually synthesizing it. These early works helped define the debate between "strong" and "weak" ALife positions.

The "strong" ALife position suggests that life is a process that can exist independently of any specific physical medium. This idea is rooted in the work of John von Neumann regarding cellular automata and universal constructors. He demonstrated that self-reproduction could be achieved by logic-based machines regardless of their physical substrate. In contrast, the "weak" ALife position argues that a true living process cannot exist outside of a chemical solution. Instead, weak ALife researchers use simulations only to understand the mechanics of biological phenomena.

Recent breakthroughs in "wet" artificial life have pushed the boundaries of what is possible. In May 2019, researchers created a new synthetic form of viable life using a variant of the bacteria Escherichia coli. They achieved this by reducing the natural number of codons in the bacterial genome from 64 to 59. In 2020, Sam Kriegman and Douglas Blackiston created a biological robot aided by artificial intelligence. By 2021, they developed Xenobots, which were the first biological robots capable of kinematic self-replication. These organisms do not use traditional birth; instead, they collect loose cells to assemble new copies of themselves.

One of the biggest goals in this field is achieving Open-Ended Evolution (OEE). OEE is the ability of a system to continually produce new, complex, and adaptive behaviors. A system with OEE does not reach a stable end-point or a predefined finish line. While natural life shows this quality, current artificial systems have not yet fully replicated it. Scientists continue to ask how life can arise from nonliving matter and how intelligence might emerge in artificial living systems.

Mathematical models for complex systems
Mathematical models for complex systems
Logical deterministic individual-based cellular automata model of single species population growth
Logical deterministic individual-based cellular automata model of single species population growth
Logical deterministic individual-based cellular automata model of interspecific competition for a single limited resource
Logical deterministic individual-based cellular automata model of interspecific competition for a single limited resource
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File:Unevolved swimbots showing genetic diversity.png
Unevolved swimbots showing genetic diversity.png
File:Braitenberg vehicle (simulation made with breve).jpg
Braitenberg vehicle (simulation made with...
File:Mathematical models for complex systems.jpg
Mathematical models for complex systems.jpg
File:Logical deterministic individual-based cellular automata model of single species population growth.gif
Logical deterministic individual-based...
File:Logical deterministic individual-based cellular automata model of interspecific competition for a single limited resource.gif
Logical deterministic individual-based...
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