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Artificial intelligence

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. The central problems (or goals) of AI research include reasoning, knowledge, planning, learning, natural language processing (communication), perception and the ability to move and manipulate objects.[6] General intelligence is still among the field's long-term goals.[7] Currently popular approaches include statistical methods, computational intelligence and traditional symbolic AI. History[edit] Research[edit] Goals[edit] Planning[edit] Logic-based

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

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History of artificial intelligence The history of artificial intelligence (AI) began in antiquity, with myths, stories and rumors of artificial beings endowed with intelligence or consciousness by master craftsmen; as Pamela McCorduck writes, AI began with "an ancient wish to forge the gods." The seeds of modern AI were planted by classical philosophers who attempted to describe the process of human thinking as the mechanical manipulation of symbols. This work culminated in the invention of the programmable digital computer in the 1940s, a machine based on the abstract essence of mathematical reasoning. This device and the ideas behind it inspired a handful of scientists to begin seriously discussing the possibility of building an electronic brain. The field of AI research was founded at a conference on the campus of Dartmouth College in the summer of 1956.

Outline of artificial intelligence The following outline is provided as an overview of and topical guide to artificial intelligence: Artificial intelligence (AI) – branch of computer science that deals with intelligent behavior, learning, and adaptation in machines. Research in AI is concerned with producing machines to automate tasks requiring intelligent behavior. Branches of artificial intelligence[edit] Some applications of artificial intelligence[edit] Philosophy of artificial intelligence[edit] 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.

gazebo External Documentation This is primarily a third party wrapper package with external documentation. Core Gazebo-ROS Plugins In addition to including a stable version of Gazebo, this package package builds two core plugins for integrating Gazebo with ROS. 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]

Category:Artificial intelligence From Wikipedia, the free encyclopedia Subcategories This category has the following 32 subcategories, out of 32 total. Pages in category "Artificial intelligence" 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. Computer science[edit] AI researchers have created many tools to solve the most difficult problems in computer science.

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: Description of the sum-product algorithm[edit] Variants of the Belief propagation algorithm exist for several types of graphical models (bayesian network and markov random field,[5] in particular).

ROS: stacks news Development on our OpenNI/ROS integration for the Kinect and PrimeSense Developers Kit 5.0 device continues as a fast pace. For those of you participating in the contest or otherwise hacking away, here's a summary of what's new. As always, contributions/patches are welcome. Driver Updates: Bayer Images, New point cloud and resolution options via dynamic_reconfigure BackPropagation of Error algorithm proof The algorithm derivation below can be found in Brierley [1] and Brierley and Batty [2]. Please refer to these for a hard copy. This idea was first described by Werbos [3] and popularised by Rumelhart et al.[4]. Fig 1 A multilayer perceptron Consider the network above, with one layer of hidden neurons and one output neuron.

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