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Swarm intelligence

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]

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

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Artificial intelligence AI research is highly technical and specialized, and is deeply divided into subfields that often fail to communicate with each other.[5] Some of the division is due to social and cultural factors: subfields have grown up around particular institutions and the work of individual researchers. AI research is also divided by several technical issues. Some subfields focus on the solution of specific problems. Others focus on one of several possible approaches or on the use of a particular tool or towards the accomplishment of particular applications.

Arthur Koestler, Some general properties of self-regulating open hierarchic order (1969) 1. The holon 1.1 The organism in its structural aspect is not an aggregation of elementary parts, and in its functional aspects not a chain of elementary units of behaviour. 1.2 The organism is to be regarded as a multi-levelled hierarchy of semi-autonomous sub-wholes, branching into sub-wholes of a lower order, and so on. Sub-wholes on any level of the hierarchy are referred to as holons.

Ants - learning from the collective "Go to the ant, thou sluggard," King Solomon advised in the Book of Proverbs Chapter Six, "consider her ways and be wise". Humans have always looked at the little beasts - so efficient, so purposeful and yet so different from us - and puzzled over what they have to tell us. The cultural historian Charlotte Sleigh, author of Ant (Reaktion Books 2003) says that in every age we have re-interpreted the mysteries of the ant colony to suit our own ideas. "The Victorians were very impressed with the ants, and they were keen to learn from the ants and behave more like them," she wrote. "They particularly liked the fact that the ants worked so very hard, and also the fact that they helped one another; they exercised what the Victorians called 'mutual aid'." Having told us to consider the ant, Solomon points out that the ant "having no guide, overseer or ruler, provideth her meat in the summer, and gathereth her food in the harvest".

Belief propagation Belief propagation, also known as sum-product message passing is a message passing algorithm for performing inference on graphical models, such as Bayesian networks and Markov random fields. It calculates the marginal distribution for each unobserved node, conditional on any observed nodes. Belief propagation is commonly used in artificial intelligence and information theory and has demonstrated empirical success in numerous applications including low-density parity-check codes, turbo codes, free energy approximation, and satisfiability.[1] If X=(Xv) is a set of discrete random variables with a joint mass function p, the marginal distribution of a single Xi is simply the summation of p over all other variables:

holon English[edit] Etymology[edit] particle: hole +‎ -onsociology term: holo- +‎ -on, from Ancient Greek ὅλος ‎(hólos, “whole”) with the suffix -on suggesting a particle or part. Collective intelligence: Ants and brain's neurons CONTACT: Stanford University News Service (415) 723-2558 Collective intelligence: Ants and brain's neurons STANFORD - An individual ant is not very bright, but ants in a colony, operating as a collective, do remarkable things. A single neuron in the human brain can respond only to what the neurons connected to it are doing, but all of them together can be Immanuel Kant. That resemblance is why Deborah M. Gordon, Stanford University assistant professor of biological sciences, studies ants.

Artificial consciousness Artificial consciousness (AC), also known as machine consciousness (MC) or synthetic consciousness (Gamez 2008; Reggia 2013), is a field related to artificial intelligence and cognitive robotics whose aim is to "define that which would have to be synthesized were consciousness to be found in an engineered artifact" (Aleksander 1995). Neuroscience hypothesizes that consciousness is generated by the interoperation of various parts of the brain, called the neural correlates of consciousness or NCC. Proponents of AC believe it is possible to construct machines (e.g., computer systems) that can emulate this NCC interoperation. Artificial consciousness can be viewed as an extension to artificial intelligence, assuming that the notion of intelligence in its commonly used sense is too narrow to include all aspects of consciousness.

The end of capitalism has begun The red flags and marching songs of Syriza during the Greek crisis, plus the expectation that the banks would be nationalised, revived briefly a 20th-century dream: the forced destruction of the market from above. For much of the 20th century this was how the left conceived the first stage of an economy beyond capitalism. The force would be applied by the working class, either at the ballot box or on the barricades. The lever would be the state. The opportunity would come through frequent episodes of economic collapse. Instead over the past 25 years it has been the left’s project that has collapsed. Swarm Intelligence: Are digital ants the answer to malware? Modeling Nature may be the best way to solve the malware problem. Learn how digital ants could be the answer. One of my favorite topics is anti-malware technology, especially when it portends "outside-the-box" thinking. Collective Intelligence, leveraged in Cloud Antivirus is one such example. Recently, I came across another interesting concept and it's definitely unconventional. PNNL's research

Fuzzy operator This article is definitively not a tutorial on fuzzy logic. It's simply refers a category of usefull images to help writing wiki articles on fuzzy logic operators. Only, very short comments are thus provided here. Fuzzyfication[edit] linear[edit] non linear[edit] Applications of artificial intelligence Artificial intelligence has been used in a wide range of fields including medical diagnosis, stock trading, robot control, law, remote sensing, scientific discovery and toys. However, many AI applications are not perceived as AI: "A lot of cutting edge AI has filtered into general applications, often without being called AI because once something becomes useful enough and common enough it's not labeled AI anymore," Nick Bostrom reports.[1] "Many thousands of AI applications are deeply embedded in the infrastructure of every industry." In the late 90s and early 21st century, AI technology became widely used as elements of larger systems, but the field is rarely credited for these successes.

See Ya Later, Capitalism — the Collaborative Economy Is Taking Over — Backchannel The old model of unwieldy behemoths is giving way to a new one of collaboration. Welcome to the world of Peers. Compare the phone you had as a kid to the one in your pocket now. The first was a telephone owned by a monopoly that you used at most only minutes a day. 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. Even more recently the swarm paradigm has been applied to a broader range of studies, opening up new ways of thinking about theoretical biology, economics and philosophy.

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