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.
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.
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.
This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.
We will give an overview of the field of Artificial Life (Alife). We study questions such as emergence of complexity, self-reproduction, evolution, both through concrete models and through mathematica
Students will acquire an integrative view on biological and artificial algorithms for controlling autonomous behaviors. Students will synthesize and apply this knowledge in oral presentations and comp
The course describes theories, methods, and technologies for designing robots and artificial systems inspired by evolution, development, and learning. It also shows how robotic models can help to unde
Tierra is a computer simulation developed by ecologist Thomas S. Ray in the early 1990s in which computer programs compete for time (central processing unit (CPU) time) and space (access to main memory). In this context, the computer programs in Tierra are considered to be evolvable and can mutate, self-replicate and recombine. Tierra's virtual machine is written in C. It operates on a custom instruction set designed to facilitate code changes and reordering, including features such as jump to template (as opposed to the relative or absolute jumps common to most instruction sets).
Avida is an artificial life software platform to study the evolutionary biology of self-replicating and evolving computer programs (digital organisms). Avida is under active development by Charles Ofria's Digital Evolution Lab at Michigan State University; the first version of Avida was designed in 1993 by Ofria, Chris Adami and C. Titus Brown at Caltech, and has been fully reengineered by Ofria on multiple occasions since then. The software was originally inspired by the Tierra system.
A multi-agent system (MAS or "self-organized system") is a computerized system composed of multiple interacting intelligent agents. Multi-agent systems can solve problems that are difficult or impossible for an individual agent or a monolithic system to solve. Intelligence may include methodic, functional, procedural approaches, algorithmic search or reinforcement learning. Despite considerable overlap, a multi-agent system is not always the same as an agent-based model (ABM).
On propose dans ce MOOC de se former à et avec Thymio :
apprendre à programmer le robot Thymio et ce faisant, s’initier
à l'informatique et la robotique.
In diesem Kurs handelt es sich um das Verständnis der grundlegenden Mechanismen eines Roboters wie Thymio, seiner Programmierung mit verschiedenen Sprachen und seiner Verwendung im Unterricht mit den
In diesem Kurs handelt es sich um das Verständnis der grundlegenden Mechanismen eines Roboters wie Thymio, seiner Programmierung mit verschiedenen Sprachen und seiner Verwendung im Unterricht mit den
The convergence of human and artificial intelligence is currently receiving considerable scholarly attention. Much debate about the resulting Hybrid Minds focuses on the integration of artificial intelligence into the human brain through intelligent brain- ...
[0054] Methods are provided for estimating key reliably and accurately predicting cardiac output using a limited set of non-invasively monitored physiologic inputs, and a calibrated one- dimensional arterial tree model, a database of synthetic data generat ...
As the field of ethology advances, especially over the past two decades, the role of animal-robot interaction tools has increasingly become essential. This importance arises from the need for controlled, repetitive, repeatable, and long-duration experiment ...