Glossary of artificial intelligence

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This glossary of artificial intelligence terms is about artificial intelligence, its sub-disciplines, and related fields.





  • Darkforest – is a computer go program developed by Facebook, based on deep learning techniques using a convolutional neural network. Its updated version Darkfores2 combines the techniques of its predecessor with Monte Carlo tree search.[114][115] The MCTS effectively takes tree search methods commonly seen in computer chess programs and randomizes them.[116] With the update, the system is known as Darkfmcts3.[117]
  • Dartmouth workshop – The Dartmouth Summer Research Project on Artificial Intelligence was the name of a 1956 summer workshop now considered by many[118][119] (though not all[120]) to be the seminal event for artificial intelligence as a field.
  • Data fusion – is the process of integrating multiple data sources to produce more consistent, accurate, and useful information than that provided by any individual data source.[121]
  • Data integration – involves combining data residing in different sources and providing users with a unified view of them.[122] This process becomes significant in a variety of situations, which include both commercial (such as when two similar companies need to merge their databases) and scientific (combining research results from different bioinformatics repositories, for example) domains. Data integration appears with increasing frequency as the volume (that is, big data[123]) and the need to share existing data explodes.[124] It has become the focus of extensive theoretical work, and numerous open problems remain unsolved.
  • Data mining – is the process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems.
  • Data science – is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from data in various forms, both structured and unstructured,[125][126] similar to data mining. Data science is a "concept to unify statistics, data analysis, machine learning and their related methods" in order to "understand and analyze actual phenomena" with data.[127] It employs techniques and theories drawn from many fields within the context of mathematics, statistics, information science, and computer science.
  • Data set – (or dataset) is a collection of data. Most commonly a data set corresponds to the contents of a single database table, or a single statistical data matrix, where every column of the table represents a particular variable, and each row corresponds to a given member of the data set in question. The data set lists values for each of the variables, such as height and weight of an object, for each member of the data set. Each value is known as a datum. The data set may comprise data for one or more members, corresponding to the number of rows.
  • Data warehouse – (DW or DWH), also known as an enterprise data warehouse (EDW), is a system used for reporting and data analysis.[128] DWs are central repositories of integrated data from one or more disparate sources. They store current and historical data in one single place[129]
  • Datalog – is a declarative logic programming language that syntactically is a subset of Prolog. It is often used as a query language for deductive databases. In recent years, Datalog has found new application in data integration, information extraction, networking, program analysis, security, and cloud computing.[130]
  • Decision boundary – In the case of backpropagation based artificial neural networks or perceptrons, the type of decision boundary that the network can learn is determined by the number of hidden layers the network has. If it has no hidden layers, then it can only learn linear problems. If it has one hidden layer, then it can learn any continuous function on compact subsets of Rn as shown by the Universal approximation theorem, thus it can have an arbitrary decision boundary.
  • Decision support system – (DSS), is an information system that supports business or organizational decision-making activities. DSSs serve the management, operations and planning levels of an organization (usually mid and higher management) and help people make decisions about problems that may be rapidly changing and not easily specified in advance—i.e. unstructured and semi-structured decision problems. Decision support systems can be either fully computerized or human-powered, or a combination of both.
  • Decision theory – (or the theory of choice) is the study of the reasoning underlying an agent's choices.[131] Decision theory can be broken into two branches: normative decision theory, which gives advice on how to make the best decisions given a set of uncertain beliefs and a set of values, and descriptive decision theory which analyzes how existing, possibly irrational agents actually make decisions.
  • Decision tree learning – uses a decision tree (as a predictive model) to go from observations about an item (represented in the branches) to conclusions about the item's target value (represented in the leaves). It is one of the predictive modeling approaches used in statistics, data mining and machine learning.
  • Declarative programming – is a programming paradigm—a style of building the structure and elements of computer programs—that expresses the logic of a computation without describing its control flow.[132]
  • Deductive classifier – is a type of artificial intelligence inference engine. It takes as input a set of declarations in a frame language about a domain such as medical research or molecular biology. For example, the names of classes, sub-classes, properties, and restrictions on allowable values. Compared to rule-based inference engines, which can only apply triggers like OFF or IF-THEN when a condition is not met, these classifiers seek to mimic human deductive logic.[133]
  • Deep Blue – was a chess-playing computer developed by IBM. It is known for being the first computer chess-playing system to win both a chess game and a chess match against a reigning world champion under regular time controls.
  • Deep learning – (also known as deep structured learning or hierarchical learning) is part of a broader family of machine learning methods based on learning data representations, as opposed to task-specific algorithms. Learning can be supervised, semi-supervised or unsupervised.[134][135][136]
  • DeepMindDeepMind Technologies is a British artificial intelligence company founded in September 2010, currently owned by Alphabet Inc. The company is based in London, with research centres in Canada,[137] France,[138] and the United States. Acquired by Google in 2014, the company has created a neural network that learns how to play video games in a fashion similar to that of humans,[139] as well as a Neural Turing machine,[140] or a neural network that may be able to access an external memory like a conventional Turing machine, resulting in a computer that mimics the short-term memory of the human brain.[141][142] The company made headlines in 2016 after its AlphaGo program beat a human professional Go player Lee Sedol, the world champion, in a five-game match, which was the subject of a documentary film.[143] A more general program, AlphaZero, beat the most powerful programs playing go, chess and shogi (Japanese chess) after a few days of play against itself using reinforcement learning.[144]
  • Default logic – is a non-monotonic logic proposed by Raymond Reiter to formalize reasoning with default assumptions.
  • Description logicDescription logics (DL) are a family of formal knowledge representation languages. Many DLs are more expressive than propositional logic but less expressive than first-order logic. In contrast to the latter, the core reasoning problems for DLs are (usually) decidable, and efficient decision procedures have been designed and implemented for these problems. There are general, spatial, temporal, spatiotemporal, and fuzzy descriptions logics, and each description logic features a different balance between DL expressivity and reasoning complexity by supporting different sets of mathematical constructors.[145]
  • Developmental robotics – (DevRob), sometimes called epigenetic robotics, is a scientific field which aims at studying the developmental mechanisms, architectures and constraints that allow lifelong and open-ended learning of new skills and new knowledge in embodied machines.
  • Diagnosis – is concerned with the development of algorithms and techniques that are able to determine whether the behaviour of a system is correct. If the system is not functioning correctly, the algorithm should be able to determine, as accurately as possible, which part of the system is failing, and which kind of fault it is facing. The computation is based on observations, which provide information on the current behaviour.
  • Dialogue system – or conversational agent (CA), is a computer system intended to converse with a human with a coherent structure. Dialogue systems have employed text, speech, graphics, haptics, gestures, and other modes for communication on both the input and output channel.
  • Dimensionality reduction – or dimension reduction, is the process of reducing the number of random variables under consideration[146] by obtaining a set of principal variables. It can be divided into feature selection and feature extraction.[147]
  • Discrete system – is a system with a countable number of states. Discrete systems may be contrasted with continuous systems, which may also be called analog systems. A final discrete system is often modeled with a directed graph and is analyzed for correctness and complexity according to computational theory. Because discrete systems have a countable number of states, they may be described in precise mathematical models. A computer is a finite state machine that may be viewed as a discrete system. Because computers are often used to model not only other discrete systems but continuous systems as well, methods have been developed to represent real-world continuous systems as discrete systems. One such method involves sampling a continuous signal at discrete time intervals.
  • Distributed artificial intelligence(DAI), also called Decentralized Artificial Intelligence,[148] is a subfield of artificial intelligence research dedicated to the development of distributed solutions for problems. DAI is closely related to and a predecessor of the field of Multi-Agent Systems.
  • Dynamic epistemic logic(DEL), is a logical framework dealing with knowledge and information change. Typically, DEL focuses on situations involving multiple agents and studies how their knowledge changes when events occur.





  • Heuristic – is a technique designed for solving a problem more quickly when classic methods are too slow, or for finding an approximate solution when classic methods fail to find any exact solution. This is achieved by trading optimality, completeness, accuracy, or precision for speed. In a way, it can be considered a shortcut. A heuristic function, also called simply a heuristic, is a function that ranks alternatives in search algorithms at each branching step based on available information to decide which branch to follow. For example, it may approximate the exact solution.[177]
  • Hidden layer – an internal layer of neurons in an artificial neural network, not dedicated to input or output
  • Hidden unit – an neuron in a hidden layer in an artificial neural network
  • Hyper-heuristic – is a heuristic search method that seeks to automate, often by the incorporation of machine learning techniques, the process of selecting, combining, generating or adapting several simpler heuristics (or components of such heuristics) to efficiently solve computational search problems. One of the motivations for studying hyper-heuristics is to build systems which can handle classes of problems rather than solving just one problem.[178][179][180]



















See also

References and notes

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