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Stigmergy is a mechanism of indirect coordination between agents or actions.[1] The principle is that the trace left in the environment by an action stimulates the performance of a next action, by the same or a different agent. In that way, subsequent actions tend to reinforce and build on each other, leading to the spontaneous emergence of coherent, apparently systematic activity. Stigmergy is a form of self-organization. It produces complex, seemingly intelligent structures, without need for any planning, control, or even direct communication between the agents. As such it supports efficient collaboration between extremely simple agents, who lack any memory, intelligence or even individual awareness of each other.[1] History[edit] The term "stigmergy" was introduced by French biologist Pierre-Paul Grassé in 1959 to refer to termite behavior. Stigmergy is now one of the key[4] concepts in the field of swarm intelligence. Stigmergic behavior in lower organisms[edit] Applications[edit] Related:  Swam IntelligenceSTIGMERGY / COMPLEX ADAPTIVE SYSTEMS

Stigmergic Simulations | manwithoutqualities Here are some terrific stigmergic simulations by architectural student Yang Chenghan that I chanced across: The first is a 3D simulation deploying 45-70 agents (source code) The second a 2D simulation deploying 20-30 agents (source code) Here are some great synthetic stigmergic stills Yang has created. Collective Intelligence in Social Insects It wasn't so long ago that the waggledance of the honey bee, the nest-building of the social wasp, and the construction of the termite mound were considered a somewhat magical aspect of nature. How could these seemingly uncommunicative, certainly very simple creatures be responsible for such epic feats of organisation and creativity? Over the last fifty years biologists have unravelled many of the mysteries surrounding social insects, and the last decade has seen an explosion of research in fields variously referred to as Collective Intelligence, Swarm Intelligence and emergent behaviour. In the Beginning Konrad Lorenz (1903-1989) is widely credited as being the father of ethology, the study of animal behaviour, with his early work on imprinting and instinctive behaviour, however it might be argued that an even earlier pioneer of the field was a South African, Eugène Marais (1872-1936). Like many geniuses, Marais' life ended in tragedy. Stigmergy: Invisible Writing Self Organisation

Swarm intelligence Swarm intelligence (SI) is the collective behavior of decentralized, self-organized systems, natural or artificial. The concept is employed in work on artificial intelligence. The expression was introduced by Gerardo Beni and Jing Wang in 1989, in the context of cellular robotic systems.[1] The application of swarm principles to robots is called swarm robotics, while 'swarm intelligence' refers to the more general set of algorithms. 'Swarm prediction' has been used in the context of forecasting problems. Example algorithms[edit] Particle swarm optimization[edit] Ant colony optimization[edit] Artificial bee colony algorithm[edit] Artificial bee colony algorithm (ABC) is a meta-heuristic algorithm introduced by Karaboga in 2005,[5] and simulates the foraging behaviour of honey bees. Bacterial colony optimization[edit] Differential evolution[edit] Differential evolution is similar to genetic algorithm and pattern search. The bees algorithm[edit] Artificial immune systems[edit] Bat algorithm[edit]

Main Page The use of Swarm Intelligence to generate architectural form Swarm modelling. The use of Swarm Intelligence to generate architectural form. Pablo Miranda Carranza Dipl ArchMSc CECA University of EastLondon Holbrook rd Stratford London E15 3EA e-mail: Paul Coates AA Dipl CECA University of EastLondon e-mail: Abstract The reason for choosing swarms as a study case is the fascination of the simplicity of its mechanics and its complexity as a phenomenon. This paper describes the swarms understanding them as examples of sensori-motor intelligence. In general the paper discusses the morphogenetic properties of swarm behaviour, and presents an example of mapping trajectories in the space of forms onto 3d flocking boids. Earlier work with autonomous agents at CECA [27, 28] were concerned with the behaviour of agents embedded in an environment, and interactions between perceptive agents and their surrounding form. 1. W. Inspired by Grey.W. automaton moving on an environment 1.1 Structural coupling 2. Diagram of the swarm.

Pierre-Paul Grassé Pierre-Paul Grassé Pierre-Paul Grassé (November 27, 1895, Périgueux (Dordogne) – July 9, 1985) was a French zoologist, author of over 300 publications including the influential 52-volume Traité de Zoologie. He was an expert on termites. Biography[edit] Education[edit] Grassé began his studies in Périgueux where his parents owned a small business. Grassé continued his studies in Paris, focusing exclusively on science. In 1926, Grassé became vice-director of the École supérieure de sériciculture. Teaching and research[edit] In 1929, Grassé became professor of zoology at the Université de Clermont-Ferrand. In 1935, he became an Assistant Professor at the Université de Paris where he worked alongside Germaine Cousin (1896–1992), and received the Prix Gadeau de Kerville de la Société entomologique de France for his work on orthoptera and termites. Publications[edit] He also composed the Termitologia (1982, 1983, 1984), a work in three volumes totalling over 2400 pages. Annex[edit] Works[edit]

Hebbian theory Hebbian theory is a theory in neuroscience which proposes an explanation for the adaptation of neurons in the brain during the learning process. It describes a basic mechanism for synaptic plasticity, where an increase in synaptic efficacy arises from the presynaptic cell's repeated and persistent stimulation of the postsynaptic cell. Introduced by Donald Hebb in his 1949 book The Organization of Behavior,[1] the theory is also called Hebb's rule, Hebb's postulate, and cell assembly theory. "Let us assume that the persistence or repetition of a reverberatory activity (or "trace") tends to induce lasting cellular changes that add to its stability.… When an axon of cell A is near enough to excite a cell B and repeatedly or persistently takes part in firing it, some growth process or metabolic change takes place in one or both cells such that A's efficiency, as one of the cells firing B, is increased Hebbian engrams and cell assembly theory[edit] Principles[edit] where to neuron and . the . .

Wiki A wiki (/ˈwɪki/ ( listen) WIK-ee) is a website on which users collaboratively modify content and structure directly from the web browser. In a typical wiki, text is written using a simplified markup language and often edited with the help of a rich-text editor.[1] A wiki is run using wiki software, otherwise known as a wiki engine. The online encyclopedia project Wikipedia is the most popular wiki-based website, and is one of the most widely viewed sites in the world, having been ranked in the top ten since 2007.[3] Wikipedia is not a single wiki but rather a collection of hundreds of wikis, one for each language. Characteristics Ward Cunningham and co-author Bo Leuf, in their book The Wiki Way: Quick Collaboration on the Web, described the essence of the Wiki concept as follows:[8] Editing Wikis have favoured plain-text editing, with fewer and simpler conventions than HTML, for indicating style and structure. Linking and creating pages Searching History Alternative definitions Implementations

stickmergy philadelphia PENNSYLVANIA suckerPUNCH: describe your project. so SUGITA / dwight ENGEL / dale SUTTLE: Stickmergy is built by two competing but dependent agent-based systems. The first uses the interaction of its agents, or people, to define spaces in the building. People of different intentions are attracted to each other and together define programmatic spaces at a point in time. The spaces are solidified by the second system– randomly moving particles that generate a 4″x4″x6′ stick when they come in contact with a programmatic space. Single sticks can be removed by the people, but as the number of sticks increases, together they are able to interfere with the movement of the people agents and permanently define a wall or floor. sP: what or who influenced this project? dS: Cecil Balmond, Roland Snooks, Agent-based Design, Stigmergy, Swarms, Self-organization, Network theory, Systems theory. sP: what were you reading/listening to/watching while developing this project? Additional credits:

Emergence in stigmergic and complex adaptive systems: A formal discrete event systems perspective Volume 21, March 2013, Pages 22–39 Stigmergy in the Human Domain Edited By Margery J. Doyle and Leslie Marsh Abstract Complex systems have been studied by researchers from every discipline: biology, chemistry, physics, sociology, mathematics and economics and more. Keywords Stigmergy; Complex adaptive systems; Emergence; Self-organization; DEVS; Dynamic structure; Scale-free networks; Artificial systems Copyright © 2012 Elsevier B.V.

Engram (neuropsychology) Engrams are a means by which memory traces are stored[1] as biophysical or biochemical changes in the brain (and other neural tissue) in response to external stimuli. They are also sometimes thought of as a neural network or fragment of memory, sometimes using a hologram analogy to describe its action in light of results showing that memory appears not to be localized in the brain. The existence of engrams is posited by some scientific theories to explain the persistence of memory and how memories are stored in the brain. The existence of neurologically defined engrams is not significantly disputed, though their exact mechanism and location has been a focus of persistent research for many decades. The term engram was coined by the little-known but influential memory researcher Richard Semon. Karl S. In Lashley's experiments (1929, 1950), rats were trained to run a maze. Later, Richard F. One region that Thompson's group studied was the lateral interpositus nucleus (LIP).

Main Page - Wikimania swarm urbanism | BLACK ROOM 641A Some inspiration from kokkugia… From the project Swarm Urbanism… “Agency operates through two main processes within this proposal: firstly by using design agents to self-organise urban matter and secondly encoding intelligence into urban elements and topologies.” “Agents within this system are not generic, instead there is an ecology of agent systems which interact, each set of agents programmed with their own desires and information.” There are two key points here that they use to relate a swarm model to urban phenomena. Second, there is a hierarchy of agents, each performing their own task. I think both of these points are crucial when starting to think about how swarm models can be applied to think of the organization of a city. -dn Like this: Like Loading...

A search engine for social networks based on the behavior of ants Research at Carlos III University in Madrid is developing an algorithm, based on ants' behavior when they are searching for food, which accelerates the search for relationships among elements that are present in social networks. One of the main technical questions in the field of social networks, whose use is becoming more and more generalized, consists in locating the chain of reference that leads from one person to another, from one node to another. The greatest challenges that are presented in this area is the enormous size of these networks and the fact that the response must be rapid, given that the final user expects results in the shortest time possible. In order to find a solution to this problem, these researchers from UC3M have developed an algorithm SoSACO, which accelerates the search for routes between two nodes that belong to a graph that represents a social network. Multiple applications