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Artificial life studies the logic of living systems in artificial environments in order to gain a deeper 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. While life is, by definition, alive, artificial life is generally referred to as being confined to a digital environment and existence. Philosophy 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”.[9] 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" [10] 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. However, different opinions about artificial life's potential have arisen: The strong alife (cf. Strong AI) position states that "life is a process which can be abstracted away from any particular medium" (John von Neumann). Notably, Tom Ray declared that his program Tierra is not simulating life in a computer but synthesizing it.[citation needed] 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. Organizations Main article: Artificial life organizations Software-based - "soft" Techniques 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. Neural networks are sometimes used to model the brain of an agent. 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. Notable simulators This is a list of artificial life/digital organism simulators, organized by the method of creature definition. Name Driven By Started Ended Avida executable dna 1993 NA breve executable dna 2006 NA Creatures neural net mid-1990s Critterding neural net 2005 NA Darwinbots executable dna 2003 DigiHive executable dna 2006 2009 DOSE executable dna 2012 NA EcoSim Fuzzy Cognitive Map 2009 NA Evolve 4.0 executable dna 1996 2007 Framsticks executable dna 1996 NA Noble Ape neural net 1996 NA OpenWorm Geppetto 2011 NA Polyworld neural net 1994 NA Primordial Life executable dna 1994 2003 ScriptBots executable dna 2010 NA TechnoSphere modules 1995 Tierra executable dna early 1990s ? 3D Virtual Creature Evolution neural net 2008 NA Program-based Further information: programming game 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. Use of cellular automata is common but not required. Another example could be an artificial intelligence and multi-agent system/program. Module-based Individual modules are added to a creature. 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 which emphasize user creation and accessibility over mutation and evolution. Parameter-based 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. Neural net–based These simulations have creatures that learn and grow using neural nets or a close derivative. Emphasis is often, although not always, more on learning than on natural selection. Hardware-based - "hard" Further information: Robot Hardware-based artificial life mainly consist of robots, that is, automatically guided machines able to do tasks on their own. Biochemical-based - "wet" Further information: Synthetic biology 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". Related subjects Artificial intelligence has traditionally used a top down approach, while alife generally works from the bottom up.[11] 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.[citation needed] The following is a list of evolutionary algorithms closely related to and used in alife: Ant colony optimization Evolutionary algorithm Genetic algorithm Genetic programming Swarm intelligence Evolutionary art uses techniques and methods from artificial life to create new forms of art. Evolutionary music uses similar techniques, but applied to music instead of visual art. Abiogenesis and the origin of life sometimes employ alife methodologies as well. History Main article: History of artificial life Criticism Alife has had a controversial history. John Maynard Smith criticized certain artificial life work in 1994 as "fact-free science".[12] However, the recent publication of artificial life articles in widely read journals such as Science and Nature is evidence that artificial life techniques are becoming more accepted in the mainstream, at least as a method of studying evolution.[13] Artificial life (often abbreviated ALife or A-Life[1]) is a field of study and an associated art form which examine systems related to life, its processes, and its evolution, through the use of simulations with computer models, robotics, and biochemistry.[2] The discipline was named by Christopher Langton, an American computer scientist, in 1986.[3] There are three main kinds of alife,[4] named for their approaches: soft,[5] from software; hard,[6] from hardware; and wet, from biochemistry. Artificial life imitates traditional biology by trying to recreate some aspects of biological phenomena.[7] The term "artificial intelligence" is often used to specifically refer to soft alife.[8] |
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