Artificial Life

Figure: An engraving of the Canard Digérateur, or “Digesting Duck” created by Jacques de Vaucanson in 1739

Christopher Langton, USA 1987

Christopher Langton coined the term Artificial Life in 1987 while working for the Los Alamos National Laboratory. The novel field attempts to recreate “natural” biological phenomena in computers or “artificial” media and studies the technological implications of the “living” artifact’s creation. It dates back to the Egyptians and their use of a water clock that employed hydraulic technology. Artificial life attempts to identify the fundamentals of life itself by recreating life-like behavior on human-made systems. Langton classifies this as “life as it could be,” or “Art” + “Life” = Artificial Life, adding, Life made by Man rather than by Nature. 42 we attempt to put together systems that behave like living organisms.

Artificial Life and Artificial Intelligence can be explained in different methods and approaches. AL can be associated with Biology and a bottom-up approach, emerging from an evolutionary process. If evolution is applied correctly, intelligent systems can emerge. On the other hand, Artificial Intelligence can be associated with Psychology and a top-down approach, connecting it from a human model of intelligence. The two fields can also be explained by comparing linear and nonlinear systems. Artificial life is a linear mechanical system with straight paths and no internal or external deviation. In contrast, Artificial intelligence is nonlinear and can’t compose a system from individual studied parts into a whole. AI has focused firstly on the production of intelligent solutions, not so much on intelligent behavior; this method goes away from how intelligence is generated in natural systems. Therefore, the complex behavior created in AI comes from serial computer programming.

In contrast, Artificial intelligence does use insights from biology to explore the complexity of the interactions between information systems and structures. However, it does not intend to explain life through new systems. Langton also explored examples of computational implementation of evolutionary processes, involving some from artificial and natural selection, “Life as it could be.” First, we need to identify what it means to be intelligent or respond to external factors based on proven solutions. In other words, when humans make decisions based on something learned or stored in a memory, like a database that responds to the environment, constantly updating. This cognition is what separates humans from machines.

We eventually separated the machineness from the process, distinguishing between the material and the process responsible for the dynamic behavior. These were called formal specifications of abstract machines. Abstract machines are made up of an abstract control structure, a “program” with a sequence of simple actions. The programming language, cellular automaton, (Figure 5) “Lamgton’s ant” (Figure 4), among others, are various methods of the abstract machines. These methods lead us to the possibility that complex natural behaviors in life can be imputed in simple generators, which can be implemented in artificial life. Simply put, for artificial life, a set of functions define life, and it is “run” in platforms suitable to the set of functions, such as software that runs in different hardware. A theory derived from the use of Langton’s cellular automata was the “edge of chaos,” which falls within the theory of “complex adaptive systems”43. The “edge of chaos” hypothesis explains that the boundary between order and chaos is where complex systems can begin to emerge, as first seen in Langton’s cellular automata44. Some explorations have led to the idea that life, the brain, organisms, and cells also operate at the “edge of chaos”.

Langton believed that it is necessary to create a “nature” within the artificial world. An aspect of applying behavior generation in artificial life is the Genotype & Phenotype (Figure 2). The distinction is, genotype acts as a specification of machinery and the phenotype, the behavior of that machinery45. In other words, the genotype is a set of instructions of what makes an organism, and the phenotype is the resulting structure from the genotype’s commands.46 We are on the verge of synthesizing life artificially as our methods become more like us, and we are becoming more like our machines. Where will life go from here?47 Langton is aware AL could be beneficial, but it needs a delicacy in handling this new technology with the social implications it may bring.

Suggested readings: Christopher G Langton (1998). Artificial life: an overview. MIT Press. ISBN 0-262-62112-6.

Mohan Matthen et al. (2007). Philosophy of biology. Elsevier, 2007. ISBN 0-444-51543-7. p. 585.

Christopher G. Langton, ed. (1989). Artificial Life: The proceedings of an interdisciplinary workshop on the synthesis and simulation of living systems, held September, 1987, in Los Alamos, New Mexico. Santa Fe Institute studies in the sciences of complexity. 6. Reading, MA: Addison-Wesley. ISBN 0-201-09346-4.

Michel Khalife and Yussef Shehadeh. Creation of Fractal Imagery Based on the Template of Langton’s Ant. \

Wolfram, 2018, Langton’s Ant, retrieved on April 6th 2018,

Wolfram, S. A New Kind of Science. Champaign, IL: Wolfram Media, pp. 930-931, 2002.

Sinapayen, Lana. Introduction to Artificial Life for People who Like AI. 25.NOV.2019

Bass, Thomas A.. The Predictors: How a Band of Maverick Physicists Used Chaos Theory to Trade Their Way to a Fortune on Wall Street. United States, Henry Holt and Company, 1999.

Figure 1 (top): Level of behaviors Figure 2 (bottom): Relationship between genotype and phenotype

Figure 3 (top): Langton’s ant with 30 ants placed in the same starting location Figure 4 (bottom): Langton’s Ant