Artificial Life: Fundamentals and Applications

By Fouad Sabry

What Is Artificial Life

Researchers in the subject of artificial life analyze systems that are related to natural life, its processes, and its evolution by employing simulations with computer models, robotics, and biochemistry. Artificial life is a subfield within the field of synthetic biology. Christopher Langton, a theoretical biologist from the United States, was the one who gave the field its name in 1986. In 1987, in Los Alamos, New Mexico, Langton arranged and hosted the very first symposium on the subject matter. There are three primary categories of artificial life, all of which get their names from the methods used to create them: soft, which comes from software; hard, which comes from hardware; and wet, which comes from biochemistry. Researchers who investigate traditional biology through the lens of artificial life do so by attempting to replicate parts of biological occurrences.

How You Will Benefit

(I) Insights, and validations about the following topics:

Chapter 1: Artificial Life

Chapter 2: Conway's Game of Life

Chapter 3: Cellular Automaton

Chapter 4: Evolutionary Computation

Chapter 5: Swarm Intelligence

Chapter 6: Multi-agent System

Chapter 7: Agent-based Model

Chapter 8: Artificial Chemistry

Chapter 9: Artificial Development

Chapter 10: Von Neumann Universal Constructor

(II) Answering the public top questions about artificial life.

(III) Real world examples for the usage of artificial life in many fields.

(IV) 17 appendices to explain, briefly, 266 emerging technologies in each industry to have 360-degree full understanding of artificial life' technologies.

Who This Book Is For

Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of artificial life.

• Language: English
• Publisher: One Billion Knowledgeable
• Release date: Jul 4, 2023

Book preview

Chapter 1: Artificial life

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 study of artificial life seeks to gain a deeper understanding of the complex information processing that defines living systems by emulating the fundamental processes of living systems in artificial environments. 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.

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 conventional model of a biological system will place primary emphasis on accurately representing the system's most critical variables. 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 this point in time, the definition of life that is generally accepted does not consider any of the currently available alife simulations or software to be alive, and they do not play a role in the process of evolution that is carried out by any ecosystem. However, different opinions about artificial life's potential have arisen:

According to the strong alife position (which should not be confused with the strong AI position), 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 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. Neural nets are a technique that are typically more associated with artificial intelligence; however, they can be extremely useful when simulating the population dynamics of organisms that are able to 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

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

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. Cellular automata are frequently utilized but are not always necessary. Another example could be an artificial intelligence and multi-agent system/program.

A creature can have multiple individual modules added to it. 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.

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.

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.

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).

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

The study of life that is based on biochemical reactions is known as synthetic biology. It involves research such as the creation of synthetic DNA. The term wetware has given rise to the expansion of the term wet. 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. This was accomplished by reducing the natural number of 64 codons in the bacterial genome to 59 codons instead, in order to encode 20 amino acids. This was accomplished by reducing the natural number of 64 codons in the bacterial genome to 59 codons.

How does life originate from things that are not themselves living?

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 bounds that living systems have, and what are their potentials?

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?