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 A torus, a toroidal shape Cellular automata are often simulated on a finite grid rather than an infinite one. History
Related: Artificial Life (A-Life)
Automate cellulaireUn article de Wikipédia, l'encyclopédie libre. À gauche, une règle locale simple : une cellule passe d'un état (i) au suivant (i+1) dans le cycle d'états dès que i+1 est présent dans au moins 3 cellules voisines. À droite, le résultat (complexe) de l'application répétée de cette règle sur une grille de cellules. Un automate cellulaire consiste en une grille régulière de « cellules » contenant chacune un « état » choisi parmi un ensemble fini et qui peut évoluer au cours du temps. Étudiés en mathématiques et en informatique théorique, les automates cellulaires sont à la fois un modèle de système dynamique discret et un modèle de calcul. Exemples[modifier | modifier le code] Les automates cellulaires les plus simples[modifier | modifier le code] Chacune des cellules pouvant prendre deux états, il existe 23=8 configurations (ou motifs) possibles d'un tel voisinage. Les automates de cette famille sont dits « élémentaires ». où chaque ligne est le résultat de la ligne précédente. -uplet où : dans
Self-organizationSelf-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 The most robust and unambiguous examples of self-organizing systems are from the physics of non-equilibrium processes. Sometimes the notion of self-organization is conflated with that of the related concept of emergence, because "[t]he order from chaos, presented by Self-Organizing models, is often interpreted in terms of emergence". Properly defined, however, there may be instances of self-organization without emergence and emergence without self-organization, and it is clear from the literature that the phenomena are not the same. Self-organization usually relies on three basic ingredients: Principles of self-organization
Forget Dunbar’s Number, Our Future Is in Scoble’s NumberFebruary 16, 2009 by Hutch Carpenter Photo credit: Mark Wallace I probably don’t know about your latest job project. I don’t know what your kids are up to. But I do know you’ve got a really strong take about where social software helps companies. Why? From Wikipedia, here’s what Dunbar’s Number is: Dunbar’s number is a theoretical cognitive limit to the number of people with whom one can maintain stable social relationships. This is a recurring issue in social networks. I like to break it people down into three types. Three Types of Social Network Participants I’m oversimplifying here, but this is a useful way to segment how people view their social network participation: Close Friends: These folks view social networks as sites for staying up to date on a limited set of close connections. Information Seekers: These folks, including me, expand beyond those with whom they have a pre-existing connection. Power Networkers: These folks amass thousands of connections. Then there are the rest of us.
Neural networkAn 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. Like other machine learning methods - systems that learn from data - neural networks have been used to solve a wide variety of tasks that are hard to solve using ordinary rule-based programming, including computer vision and speech recognition. Background There is no single formal definition of what an artificial neural network is. History and
The Nature of Code“To play life you must have a fairly large checkerboard and a plentiful supply of flat counters of two colors. It is possible to work with pencil and graph paper but it is much easier, particularly for beginners, to use counters and a board.” — Martin Gardner, Scientific American (October 1970) In this chapter, we’re going to take a break from talking about vectors and motion. In fact, the rest of the book will mostly focus on systems and algorithms (albeit ones that we can, should, and will apply to moving bodies). In the previous chapter, we encountered our first Processing example of a complex system: flocking. 7.1 What Is a Cellular Automaton? First, let’s get one thing straight. In Chapters 1 through 6, our objects (mover, particle, vehicle, boid) generally existed in only one “state.” A cellular automaton is a model of a system of “cell” objects with the following characteristics. The cells live on a grid. Figure 7.1 7.2 Elementary Cellular Automata 1) Grid. Figure 7.2 2) States.
Encyclopedia of Complexity and Systems ScienceAssembles for the first time the concepts and tools for analyzing complex systems in a wide range of fields Reflects the real world by integrating complexity with the deterministic equations and concepts that define matter, energy, and the four forces identified in nature Benefits a broad audience: undergraduates, researchers and practitioners in mathematics and many related fields Encyclopedia of Complexity and Systems Science provides an authoritative single source for understanding and applying the concepts of complexity theory together with the tools and measures for analyzing complex systems in all fields of science and engineering. The science and tools of complexity and systems science include theories of self-organization, complex systems, synergetics, dynamical systems, turbulence, catastrophes, instabilities, nonlinearity, stochastic processes, chaos, neural networks, cellular automata, adaptive systems, and genetic algorithms. Content Level » Research Show all authors
The RoboEarth Cloud EngineDigital organismHistory 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. See also Related topics and overviews Specific programs References Further reading O'Neill, B. (2003).