Swarm robotics Swarm of open-source Jasmine micro-robots recharging themselves Swarm robotics is a new approach to the coordination of multirobot systems which consist of large numbers of mostly simple physical robots. It is supposed that a desired collective behavior emerges from the interactions between the robots and interactions of robots with the environment. This approach emerged on the field of artificial swarm intelligence, as well as the biological studies of insects, ants and other fields in nature, where swarm behaviour occurs. Definition[edit] The research of swarm robotics is to study the design of robots, their physical body and their controlling behaviors. Unlike distributed robotic systems in general, swarm robotics emphasizes a large number of robots, and promotes scalability, for instance by using only local communication. Video tracking is an essential tool for systematically studying swarm-behavior, even though other tracking methods are available. Goals and applications[edit]
The Wisdom of Crowds The Wisdom of Crowds: Why the Many Are Smarter Than the Few and How Collective Wisdom Shapes Business, Economies, Societies and Nations, published in 2004, is a book written by James Surowiecki about the aggregation of information in groups, resulting in decisions that, he argues, are often better than could have been made by any single member of the group. The book presents numerous case studies and anecdotes to illustrate its argument, and touches on several fields, primarily economics and psychology. The opening anecdote relates Francis Galton's surprise that the crowd at a county fair accurately guessed the weight of an ox when their individual guesses were averaged (the average was closer to the ox's true butchered weight than the estimates of most crowd members, and also closer than any of the separate estimates made by cattle experts).[1] Types of crowd wisdom[edit] Surowiecki breaks down the advantages he sees in disorganized decisions into three main types, which he classifies as
Ant robotics Ant robotics is a special case of swarm robotics. Swarm robots are simple (and hopefully, therefore cheap) robots with limited sensing and computational capabilities. This makes it feasible to deploy teams of swarm robots and take advantage of the resulting fault tolerance and parallelism. Invention[edit] In 1991, American electrical engineer James McLurkin was the first to conceptualize the idea of "robot ants" while working at the MIT Computer Science and Artificial Intelligence Laboratory at the Massachusetts Institute of Technology. Background[edit] Researchers have developed ant robot hardware and software and demonstrated, both in simulation and on physical robots, that single ant robots or teams of ant robots solve robot-navigation tasks (such as path following[4] and terrain coverage[1][6]) robustly and efficiently. See also[edit] References[edit] ^ Jump up to: a b J. External links[edit] Ant robot by Sven KoenigAnt algorithm by Israel Wagner
Craig Reynolds (computer graphics) Craig W. Reynolds (born March 15, 1953), is an artificial life and computer graphics expert, who created the Boids artificial life simulation in 1986.[1] Reynolds worked on the film Tron (1982) as a scene programmer, and on Batman Returns (1992) as part of the video image crew. Reynolds won the 1998 Academy Scientific and Technical Award in recognition of "his pioneering contributions to the development of three-dimensional computer animation for motion picture production.
Ant colony optimization algorithms Ant behavior was the inspiration for the metaheuristic optimization technique This algorithm is a member of the ant colony algorithms family, in swarm intelligence methods, and it constitutes some metaheuristic optimizations. Initially proposed by Marco Dorigo in 1992 in his PhD thesis,[1][2] the first algorithm was aiming to search for an optimal path in a graph, based on the behavior of ants seeking a path between their colony and a source of food. The original idea has since diversified to solve a wider class of numerical problems, and as a result, several problems have emerged, drawing on various aspects of the behavior of ants. Overview[edit] Summary[edit] In the natural world, ants (initially) wander randomly, and upon finding food return to their colony while laying down pheromone trails. Over time, however, the pheromone trail starts to evaporate, thus reducing its attractive strength. Common extensions[edit] Here are some of most popular variations of ACO Algorithms. to state where to
Boids From Wikipedia, the free encyclopedia Artificial life program Separation Alignment Cohesion As with most artificial life simulations, Boids is an example of emergent behavior; that is, the complexity of Boids arises from the interaction of individual agents (the boids, in this case) adhering to a set of simple rules. separation: steer to avoid crowding local flockmatesalignment: steer towards the average heading of local flockmatescohesion: steer to move towards the average position (center of mass) of local flockmates More complex rules can be added, such as obstacle avoidance and goal seeking. The basic model has been extended in several different ways since Reynolds proposed it. The movement of Boids can be characterized as either chaotic (splitting groups and wild behaviour) or orderly. The Boids model can be used for direct control and stabilization of teams of simple unmanned ground vehicles (UGV)[5] or micro aerial vehicles (MAV)[6] in swarm robotics. See also[edit] References[edit]
Aco branches.svg - Wikipedia, the free encyclopedia From Wikimedia Commons, the free media repository Français :Choix du plus court chemin par une colonie de fourmi Auteur : Johann Dréo (User:Nojhan) Date : 27 mai 2006 Notes : 1) la première fourmi trouve la source de nourriture (F), via un chemin quelconque (a), puis revient au nid (N) en laissant derrière elle une piste de phéromone (b). 2) les fourmis empruntent indifféremment les 4 chemins possibles, mais le renforcement de la piste rend plus attractif le chemin le plus court. 3) les fourmis empruntent le chemin le plus court, les portions longues des autres chemins voient la piste de phéromones s'évaporer. English:Shortest path find by an ant colony Author: Johann Dréo (User:Nojhan) Date: 27 may 2006 Русский:Поиск кратчайшего пути муравьиной колонией Автор: Johann Dréo (User:Nojhan) Дата: 27 мая 2006 Licensing[edit] File history Click on a date/time to view the file as it appeared at that time. You cannot overwrite this file. There are no pages that link to this file. File usage on other wikis
Extraordinary Popular Delusions and the Madness of Crowds A satirical "Bubble card" An Alchemist "Witch Hunter", Matthew Hopkins The Cock Lane "ghost" Extraordinary Popular Delusions and the Madness of Crowds is a history of popular folly by Scottish journalist Charles Mackay, first published in 1841. In later editions Mackay added a footnote referencing the Railway Mania of the 1840s as another "popular delusion", of importance at least comparable with the South Sea Bubble. Volume I[edit] Economic bubbles[edit] Among the bubbles or financial manias described by Mackay are the South Sea Company bubble of 1711–1720, the Mississippi Company bubble of 1719–1720, and the Dutch tulip mania of the early seventeenth century. Two modern researchers, Peter Garber and Anne Goldgar, independently conclude that Mackay greatly exaggerated the scale and effects of the Tulip bubble,[6] and Mike Dash, in a footnote to his modern popular history of the alleged bubble states that he believes the importance and extent of the tulip mania was overstated.[7] Notes
Travelling salesman problem The travelling salesman problem (TSP) asks the following question: Given a list of cities and the distances between each pair of cities, what is the shortest possible route that visits each city exactly once and returns to the origin city? It is an NP-hard problem in combinatorial optimization, important in operations research and theoretical computer science. Solution of a travelling salesman problem TSP is a special case of the travelling purchaser problem. In the theory of computational complexity, the decision version of the TSP (where, given a length L, the task is to decide whether the graph has any tour shorter than L) belongs to the class of NP-complete problems. The problem was first formulated in 1930 and is one of the most intensively studied problems in optimization. The TSP has several applications even in its purest formulation, such as planning, logistics, and the manufacture of microchips. History[edit] The origins of the travelling salesman problem are unclear. Richard M.
Knapsack problem Example of a one-dimensional (constraint) knapsack problem: which boxes should be chosen to maximize the amount of money while still keeping the overall weight under or equal to 15 kg? A multiple constrained problem could consider both the weight and volume of the boxes. (Answer: if any number of each box is available, then three yellow boxes and three grey boxes; if only the shown boxes are available, then all but the green box.) The knapsack problem or rucksack problem is a problem in combinatorial optimization: Given a set of items, each with a mass and a value, determine the number of each item to include in a collection so that the total weight is less than or equal to a given limit and the total value is as large as possible. It derives its name from the problem faced by someone who is constrained by a fixed-size knapsack and must fill it with the most valuable items. Applications[edit] Definition[edit] Mathematically the 0-1-knapsack problem can be formulated as: Let there be items, to
Knapsack ants.svg - Wikipedia, the free encyclopedia Summary[edit] Knapsack problem resolved using ants. Ants discover a small drop of honey, they prefer to concentrate their resources on this drop instead of moving to sugar water, in larger quantity but less interesting for the colony. This is similar to the knapsack problem where one tries to find the best items (honey vs water) to carry in a bag with limited capacity (the number of available ants or the size of the colony). Author : DakeSoftware : Inkscape Licensing[edit] Click on a date/time to view the file as it appeared at that time.
Artificial immune system In computer science, artificial immune systems (AIS) are a class of computationally intelligent systems inspired by the principles and processes of the vertebrate immune system. The algorithms typically exploit the immune system's characteristics of learning and memory to solve a problem. Definition[edit] The field of Artificial Immune Systems (AIS) is concerned with abstracting the structure and function of the immune system to computational systems, and investigating the application of these systems towards solving computational problems from mathematics, engineering, and information technology. Artificial Immune Systems (AIS) are adaptive systems, inspired by theoretical immunology and observed immune functions, principles and models, which are applied to problem solving.[1] History[edit] AIS emerged in the mid 1980s with articles authored by Farmer, Packard and Perelson (1986) and Bersini and Varela (1990) on immune networks. Techniques[edit] See also[edit] Notes[edit] References[edit]
Firefly algorithm The firefly algorithm (FA) is a metaheuristic algorithm, inspired by the flashing behaviour of fireflies. The primary purpose for a firefly's flash is to act as a signal system to attract other fireflies. Xin-She Yang formulated this firefly algorithm by assuming:[1] All fireflies are unisexual, so that one firefly will be attracted to all other fireflies;Attractiveness is proportional to their brightness, and for any two fireflies, the less bright one will be attracted by (and thus move to) the brighter one; however, the brightness can decrease as their distance increases;If there are no fireflies brighter than a given firefly, it will move randomly. The brightness should be associated with the objective function. Algorithm description[edit] The pseudo code can be summarized as: Begin 1) Objective function: ; 2) Generate an initial population of fireflies ;. 3) Formulate light intensity so that it is associated with (for example, for maximization problems, or simply and is where The . See also[edit]