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Swarm intelligence (SI) is artificial intelligence based on the collective behavior of decentralized, self-organized systems. The term " collective behavior " was first used by Robert E __FORCETOC__ Decentralization or Decentralisation (see Spelling differences) is the process of dispersing Decision-making governance closer to the people Self-organization is a process of Attraction and repulsion in which the internal organization of a System, normally an open system, increases The expression was introduced by Gerardo Beni and Jing Wang in 1989, in the context of cellular robotic systems[1]. Gerardo Beni is a Professor of Electrical engineering at University of California Riverside who with Jing Wang is known as the originator of the term Year 1989 ( MCMLXXXIX) was a Common year starting on Sunday (link displays 1989 Gregorian calendar) A cellular automaton (plural cellular automata) is a discrete model studied in computability theory, Mathematics, Theoretical biology

SI systems are typically made up of a population of simple agents interacting locally with one another and with their environment. In Artificial intelligence, an intelligent agent ( IA) is an entity which observes "reason" and acts upon an environment (i The agents follow very simple rules, and although there is no centralized control structure dictating how individual agents should behave, local interactions between such agents lead to the emergence of complex global behavior. For other uses see Emergence (disambiguation, Emergent, and Emergency. Natural examples of SI include ant colonies, bird flocking, animal herding, bacterial growth, and fish schooling. An ant colony is an underground Lair where Ants live Colonies consist of a series of underground chambers connected to each other and the surface of the earth by Flocking - the collective motion of a large number of self-propelled entities - is a behaviour exhibited by many living beings such as birds Fish, Bacteria Herding is the act of bringing individual animals together into a group ( Herd) maintaining the group and moving the group from place to place&mdashor any combination of The Bacteria ( singular: bacterium) are a large group of unicellular Microorganisms Typically a few Micrometres in length bacteria have The SWARM Remote Weapon System (Stabilised Weapon And Reconnaissance Mount is a fully armored Remote weapon system designed and built by the Thales Group in

The application of swarm principles to robots is called swarm robotics, while 'swarm intelligence' refers to the more general set of algorithms. A robot is a mechanical or Virtual Artificial agent In practice it is usually an electro-mechanical system which by its appearance or movements 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

Contents

Example algorithms

Ant colony optimization

Ant colony optimization is a class of optimization algorithms modeled on the actions of an ant colony. The ant colony optimization Algorithm (ACO introduced by Marco Dorigo in 1992 in his PhD thesis is a probabilistic technique for solving computational In Computing, optimization is the process of modifying a system to make some aspect of it work more efficiently or use fewer resources In Mathematics, Computing, Linguistics and related subjects an algorithm is a sequence of finite instructions often used for Calculation An ant colony is an underground Lair where Ants live Colonies consist of a series of underground chambers connected to each other and the surface of the earth by Artificial 'ants' - simulation agents - locate optimal solutions by moving through a parameter space representing all possible solutions. In Generative art people talk about parameter space as the set of possibleparameters for a generative system Real ants lay down pheromones directing each other to resources while exploring their environment. A pheromone (from Greek φέρω phero "to bear" + ‘ορμόνη " Hormone " is a Chemical that triggers a natural The simulated 'ants' similarly record their positions and the quality of their solutions, so that in later simulation iterations more ants locate better solutions. [2] One variation on this approach is the bees algorithm, which is more analogous to the foraging patterns of the honey bee. The Bees Algorithm is a population-based Search algorithm first developed in 2005 This article refers collectively to all true honey bees for the "common" domesticated honey bee see European honey bee Honey bees

Particle swarm optimization

Particle swarm optimization or PSO is a global optimization algorithm for dealing with problems in which a best solution can be represented as a point or surface in an n-dimensional space. Particle swarm optimization (PSO is a Swarm intelligence based Algorithm to find a solution to an optimization problem in a Search space, or model and Hypotheses are plotted in this space and seeded with an initial velocity, as well as a communication channel between the particles[3][4]. In Physics, velocity is defined as the rate of change of Position. Particles then move through the solution space, and are evaluated according to some fitness criterion after each timestep. Fitness (often denoted w in Population genetics models is a central concept in evolutionary theory. Over time, particles are accelerated towards those particles within their communication grouping which have better fitness values. The main advantage of such an approach over other global minimization strategies such as simulated annealing is that the large number of members that make up the particle swarm make the technique impressively resilient to the problem of local minima. Simulated annealing (SA is a generic probabilistic Meta-algorithm for the Global optimization problem namely locating a good approximation to the In Mathematics, maxima and minima, known collectively as extrema, are the largest value (maximum or smallest value (minimum that

Stochastic diffusion search

Stochastic Diffusion Search or SDS is an agent based on probabilistic global search and optimization technique best suited to problems where the objective function can be decomposed into multiple independent partial-functions. Stochastic Diffusion Search (SDS was first described in 1989 as a population-based pattern-matching algorithm 1989 Each agent maintains a hypothesis which is iteratively tested by evaluating a randomly selected partial objective function parameterised by the agent's current hypothesis. In the standard version of SDS such partial function evaluations are binary resulting in each agent becoming active or inactive. Information on hypotheses is diffused across the population via inter-agent communication. Unlike the stigmergic communication used in ACO, in SDS agents communicate hypotheses via a one-to-one communication strategy analogous to the tandem running procedure observed in some species of ant. Stigmergy is a mechanism of spontaneous indirect Coordination between agents or actions where the trace left in the environment by an action stimulates the A positive feedback mechanism ensures that, over time, a population of agents stabilise around the global-best solution. SDS is both an efficient and robust search and optimisation algorithm, which has been extensively mathematically described.

Applications

Swarm Intelligence-based techniques can be used in a number of applications. The U. S. military is investigating swarm techniques for controlling unmanned vehicles. The European Space Agency is thinking about an orbital swarm for self assembly and interferometry. The European Space Agency ( ESA) established in 1975 is an intergovernmental organisation dedicated to the exploration of space, currently with 17 member NASA is investigating the use of swarm technology for planetary mapping. The National Aeronautics and Space Administration ( NASA, ˈnæsə is an agency of the United States government, responsible for the nation's public space program A 1992 paper by M. Anthony Lewis and George A. M Anthony Lewis, PhD is a Robotics researcher and CEO of Iguana Robotics a company specialising in the development of Biomorphic robotics technologies Bekey[5] discusses the possibility of using swarm intelligence to control nanobots within the body for the purpose of killing cancer tumors. Artists are using swarm technology as a means of creating complex interactive systems or simulating crowds. Tim Burton's Batman Returns was the first movie to make use of swarm technology for rendering, realistically depicting the movements of a group of penguins using the Boids system. Batman Returns is a 1992 Superhero film directed by Tim Burton. Boids, developed by Craig Reynolds in 1986, is an Artificial life program simulating the flocking behaviour of Birds His paper on The Lord of the Rings film trilogy made use of similar technology, known as Massive, during battle scenes. The Lord of the Rings film trilogy consists of three Live action Fantasy Epic films The Fellowship of the Ring ( 2001 Massive (Multiple Agent Simulation System in Virtual Environment is a high-end Computer animation and Artificial intelligence software package used for generating Swarm technology is particularly attractive because it is cheap, robust, and simple.

The inherent intelligence of swarms has inspired many social and political philosophers, in that the collective movements of an aggregate often derive from independent decision making on the part of a single individual. A common example is how the unaided decision of a person in a crowd to start clapping will often encourage others to follow suit, culminating in widespread applause. Such knowledge, an individualist advocate might argue, should encourage individual decision making (however mundane) as an effective tool in bringing about widespread social change.

The use of Swarm Intelligence in Telecommunication Networks has also been researched, in the form of Ant Based Routing. This was pioneered separately by Dorigo et al and Hewlett Packard in the mid-1990s, with a number of variations since. Basically this uses a probabilistic routing table rewarding/reinforcing the route successfully traversed by each "ant" (a small control packet) which flood the network. Reinforcement of the route in the forwards, reverse direction and both simultaneously have been researched: backwards reinforcement requires a symmetric network and couples the two directions together; forwards reinforcement rewards a route before the outcome is known (but then you pay for the cinema before you know how good the film is). As the system behaves stochastically and is therefore lacking repeatability, there are large hurdles to commercial deployment.

References in popular culture

Swarm intelligence-related concepts and references can be found throughout popular culture, frequently as some form of collective intelligence or group mind involving far more agents than used in current applications. Collective intelligence is a shared or group Intelligence that emerges from the collaboration and competition of many individuals

Notable Researchers

  • Gerardo Beni
  • Marco Dorigo
  • Luca Maria Gambardella
  • Russell C. Gerardo Beni is a Professor of Electrical engineering at University of California Riverside who with Jing Wang is known as the originator of the term Marco Dorigo is a research director for the Belgian Funds for Scientific Research (FNRS and a co-director of IRIDIA the artificial intelligence lab of the Université Libre de Luca Maria Gambardella (born January 4, 1962) has been co-director of the Swiss AI lab IDSIA since 1995 Eberhart
  • James Kennedy

See also

References

  1. ^ Beni, G. James Kennedy (b November 5, 1950 -) is an American Social psychologist, best known as an originator and researcher of Particle swarm optimization Robert Jackson Marks II is a Distinguished Professor of Electrical and Computer Engineering at Baylor University and proponent of Intelligent design. Craig Reynolds (born March 15, 1953) is an Artificial life and Computer graphics expert who created the Boids artificial life simulation Ken Rinaldo (born 1958 is an American artist and educator whose work focuses on interactive Art installations that explore the intersection between natural and AgentSheets is an easy to use commercial game and simulation authoring tool The notion of antiobjects is a computational metaphor useful to conceptualize and solve hard problems by swapping computational foreground and background The Bees Algorithm is a population-based Search algorithm first developed in 2005 Boids, developed by Craig Reynolds in 1986, is an Artificial life program simulating the flocking behaviour of Birds His paper on Craig Reynolds (born March 15, 1953) is an Artificial life and Computer graphics expert who created the Boids artificial life simulation Collective intelligence is a shared or group Intelligence that emerges from the collaboration and competition of many individuals A cellular automaton (plural cellular automata) is a discrete model studied in computability theory, Mathematics, Theoretical biology Differential Evolution (DE is a method of mathematical optimization of multidimensional functions and belongs to the class of Evolution strategy optimizers For other uses see Emergence (disambiguation, Emergent, and Emergency. In Computer science evolutionary computation is a subfield of Artificial intelligence (more particularly Computational intelligence) that involves Flocking - the collective motion of a large number of self-propelled entities - is a behaviour exhibited by many living beings such as birds Fish, Bacteria Harmony search (HS is a Metaheuristic algorithm (also known as Soft computing algorithm or Evolutionary algorithm) mimicking the improvisation process Herd behaviour describes how individuals in a group can act together without planned direction 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 SwisTrack is a powerful tool for tracking Robots humans animals and objects using a Camera or a recorded Video as input source , Wang, J. Swarm Intelligence in Cellular Robotic Systems, Proceed. NATO Advanced Workshop on Robots and Biological Systems, Tuscany, Italy, June 26–30 (1989)
  2. ^ Ant Colony Optimization by Marco Dorigo and Thomas Stützle, MIT Press, 2004. ISBN 0-262-04219-3
  3. ^ Recent Approaches to Global Optimization Problems Through Particle Swarm Optimization, by Parsopoulos, K. E. , Vrahatis, M. N. , Natural Computing, 1 (2-3), pp. 235-306, 2002.
  4. ^ Particle Swarm Optimization by Maurice Clerc, ISTE, ISBN 1-905209-04-5, 2006.
  5. ^ Lewis, M. Anthony, and Bekey, George A. The Behavioral Self-Organization of Nanorobots Using Local Rules. Proceedings of the 1992 IEEE/RSJ International Conference on Intelligent Robots and Systems.

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