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Cellular automaton

Cellular automaton
The concept was originally discovered in the 1940s by Stanislaw Ulam and John von Neumann while they were contemporaries at Los Alamos National Laboratory. While studied by some throughout the 1950s and 1960s, it was not until the 1970s and Conway's Game of Life, a two-dimensional cellular automaton, that interest in the subject expanded beyond academia. In the 1980s, Stephen Wolfram engaged in a systematic study of one-dimensional cellular automata, or what he calls elementary cellular automata; his research assistant Matthew Cook showed that one of these rules is Turing-complete. Wolfram published A New Kind of Science in 2002, claiming that cellular automata have applications in many fields of science. These include computer processors and cryptography. The primary classifications of cellular automata as outlined by Wolfram are numbered one to four. Overview[edit] A torus, a toroidal shape Cellular automata are often simulated on a finite grid rather than an infinite one. History[edit]

http://en.wikipedia.org/wiki/Cellular_automaton

Related:  Artificial Life (A-Life)

Neural network An artificial neural network is an interconnected group of nodes, akin to the vast network of neurons in a brain. Here, each circular node represents an artificial neuron and an arrow represents a connection from the output of one neuron to the input of another. For example, a neural network for handwriting recognition is defined by a set of input neurons which may be activated by the pixels of an input image. After being weighted and transformed by a function (determined by the network's designer), the activations of these neurons are then passed on to other neurons. This process is repeated until finally, an output neuron is activated. This determines which character was read.

Self-Assembling Molecules Like These May Have Sparked Life on Earth - Wired Science When his students successfully converted chemical precursors into an RNA-like molecule in the form of a yellow gel, Nicholas Hud scribbled down the surprising recipe. Image: Nicholas Hud For Nicholas Hud, a chemist at the Georgia Institute of Technology, the turning point came in July of 2012 when two of his students rushed into his office with a tiny tube of gel. The contents, which looked like a blob of lemon Jell-O, represented the fruits of a 20-year effort to construct something that looked like life from the cacophony of chemicals that were available on the early Earth. To some biochemists, Hud’s attempts to find an evolutionary precursor to ribonucleic acid may have seemed a fool’s errand. The dominant theory to explain the origins of life — known as the RNA world hypothesis — regards ribonucleic acid as the first biological molecule.

Agent-based model An agent-based model (ABM) is one of a class of computational models for simulating the actions and interactions of autonomous agents (both individual or collective entities such as organizations or groups) with a view to assessing their effects on the system as a whole. It combines elements of game theory, complex systems, emergence, computational sociology, multi-agent systems, and evolutionary programming. Monte Carlo Methods are used to introduce randomness. Particularly within ecology, ABMs are also called individual-based models (IBMs),[1] and individuals within IBMs may be simpler than fully autonomous agents within ABMs. Agent-based models are a kind of microscale model [3] that simulate the simultaneous operations and interactions of multiple agents in an attempt to re-create and predict the appearance of complex phenomena. The process is one of emergence from the lower (micro) level of systems to a higher (macro) level.

Self-organization Self-organization occurs in a variety of physical, chemical, biological, robotic, social and cognitive systems. Common examples include crystallization, the emergence of convection patterns in a liquid heated from below, chemical oscillators, swarming in groups of animals, and the way neural networks learn to recognize complex patterns. Overview[edit] The most robust and unambiguous examples[1] of self-organizing systems are from the physics of non-equilibrium processes. Bees algorithm In computer science and operations research, the Bees Algorithm is a population-based search algorithm which was developed in 2005.[1] It mimics the food foraging behaviour of honey bee colonies. In its basic version the algorithm performs a kind of neighbourhood search combined with global search, and can be used for both combinatorial optimization and continuous optimization. The only condition for the application of the Bees Algorithm is that some measure of topological distance between the solutions is defined. The effectiveness and specific abilities of the Bees Algorithm have been proven in a number of studies. [2][3] The Bees Algorithm is inspired by the foraging behaviour of honey bees. Honey bees foraging strategy in nature[edit]

Digital organism History[edit] Steen Rasmussen at Los Alamos National Laboratory took the idea from Core War one step further in his core world system by introducing a genetic algorithm that automatically wrote programs. However, Rasmussen did not observe the evolution of complex and stable programs. It turned out that the programming language in which core world programs were written was very brittle, and more often than not mutations would completely destroy the functionality of a program. In 1996, Andy Pargellis created a Tierra-like system called Amoeba that evolved self-replication from a randomly seeded initial condition.

How Science Turned a Struggling Pro Skier Into an Olympic Medal Contender - Wired Science Saslong.org/R.Perathoner Steven Nyman is poised at the starting gate, alert, coiled, ready. A signal sounds: three even tones followed by a single, more urgent pitch, sending Nyman kicking onto the Val Gardena downhill ski course. He pushes five times with his poles, accelerating as quickly as possible, stabbing the snow frantically. He skates forward with abbreviated strokes, neon green boots moving up and down, his focus on building as much momentum as possible.

Fractal Figure 1a. The Mandelbrot set illustrates self-similarity. As the image is enlarged, the same pattern re-appears so that it is virtually impossible to determine the scale being examined. Figure 1b. The same fractal magnified six times.

Related:  Multi-agent system