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AI Artificial Intelligence

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Artificial intelligence
Major goals
Approaches
Philosophy
History
Technology
Glossary

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  • 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.[125][126] The MCTS effectively takes tree search methods commonly seen in computer chess programs and randomizes them.[127] With the update, the system is known as Darkfmcts3.[128]
  • Dartmouth workshop – The Dartmouth Summer Research Project on Artificial Intelligence was the name of a 1956 summer workshop now considered by many[129][130] (though not all[131]) 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.[132]
  • Data integration – involves combining data residing in different sources and providing users with a unified view of them.[133] 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) and the need to share existing data explodes.[134] 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,[135][136] 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.[137] 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.[138] DWs are central repositories of integrated data from one or more disparate sources. They store current and historical data in one single place[139]
  • 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.[140]
  • 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.[141] 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.[142]
  • 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.
  • 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.[143][144][145]
  • DeepMind – DeepMind 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,[146] France,[147] 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,[148] as well as a Neural Turing machine,[149] 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.[150][151] 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.[152] 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.[153]
  • Default logic – is a non-monotonic logic proposed by Raymond Reiter to formalize reasoning with default assumptions.
  • Description logic – Description 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.[154]
  • 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[155] by obtaining a set of principal variables. It can be divided into feature selection and feature extraction.[156]
  • 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,[157] 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.

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  • Fast-and-frugal trees – a type of classification tree. Fast-and-frugal trees can be used as decision-making tools which operate as lexicographic classifiers, and, if required, associate an action (decision) to each class or category.[172]
  • Feature extraction – In machine learning, pattern recognition and in image processing, feature extraction starts from an initial set of measured data and builds derived values (features) intended to be informative and non-redundant, facilitating the subsequent learning and generalization steps, and in some cases leading to better human interpretations.
  • Feature learning – In machine learning, feature learning or representation learning[173] is a set of techniques that allows a system to automatically discover the representations needed for feature detection or classification from raw data. This replaces manual feature engineering and allows a machine to both learn the features and use them to perform a specific task.
  • Feature selection – In machine learning and statistics, feature selection, also known as variable selection, attribute selection or variable subset selection, is the process of selecting a subset of relevant features (variables, predictors) for use in model construction.
  • Federated learning – a type of machine learning that allows for training on multiple devices with decentralized data, thus helping preserve the privacy of individual users and their data.
  • First-order logic (also known as first-order predicate calculus and predicate logic) – a collection of formal systems used in mathematics, philosophy, linguistics, and computer science. First-order logic uses quantified variables over non-logical objects and allows the use of sentences that contain variables, so that rather than propositions such as Socrates is a man one can have expressions in the form “there exists X such that X is Socrates and X is a man” and there exists is a quantifier while X is a variable.[174] This distinguishes it from propositional logic, which does not use quantifiers or relations.[175]
  • Fluent – a condition that can change over time. In logical approaches to reasoning about actions, fluents can be represented in first-order logic by predicates having an argument that depends on time.
  • Formal language – a set of words whose letters are taken from an alphabet and are well-formed according to a specific set of rules.
  • Forward chaining – (or forward reasoning) is one of the two main methods of reasoning when using an inference engine and can be described logically as repeated application of modus ponens. Forward chaining is a popular implementation strategy for expert systems, business and production rule systems. The opposite of forward chaining is backward chaining. Forward chaining starts with the available data and uses inference rules to extract more data (from an end user, for example) until a goal is reached. An inference engine using forward chaining searches the inference rules until it finds one where the antecedent (If clause) is known to be true. When such a rule is found, the engine can conclude, or infer, the consequent (Then clause), resulting in the addition of new information to its data.[176]
  • Frame – an artificial intelligence data structure used to divide knowledge into substructures by representing “stereotyped situations.” Frames are the primary data structure used in artificial intelligence frame language.
  • Frame language – a technology used for knowledge representation in artificial intelligence. Frames are stored as ontologies of sets and subsets of the frame concepts. They are similar to class hierarchies in object-oriented languages although their fundamental design goals are different. Frames are focused on explicit and intuitive representation of knowledge whereas objects focus on encapsulation and information hiding. Frames originated in AI research and objects primarily in software engineering. However, in practice the techniques and capabilities of frame and object-oriented languages overlap significantly.
  • Frame problem – is the problem of finding adequate collections of axioms for a viable description of a robot environment.[177]
  • Friendly artificial intelligence (also friendly AI or FAI) – a hypothetical artificial general intelligence (AGI) that would have a positive effect on humanity. It is a part of the ethics of artificial intelligence and is closely related to machine ethics. While machine ethics is concerned with how an artificially intelligent agent should behave, friendly artificial intelligence research is focused on how to practically bring about this behaviour and ensuring it is adequately constrained.
  • Futures studies – is the study of postulating possible, probable, and preferable futures and the worldviews and myths that underlie them.[178]
  • Fuzzy control system – a control system based on fuzzy logic—a mathematical system that analyzes analog input values in terms of logical variables that take on continuous values between 0 and 1, in contrast to classical or digital logic, which operates on discrete values of either 1 or 0 (true or false, respectively).[179][180]
  • Fuzzy logic – a simple form for the many-valued logic, in which the truth values of variables may have any degree of “Truthfulness” that can be represented by any real number in the range between 0 (as in Completely False) and 1 (as in Completely True) inclusive. Consequently, It is employed to handle the concept of partial truth, where the truth value may range between completely true and completely false. In contrast to Boolean logic, where the truth values of variables may have the integer values 0 or 1 only.
  • Fuzzy rule – Fuzzy rules are used within fuzzy logic systems to infer an output based on input variables.
  • Fuzzy set – In classical set theory, the membership of elements in a set is assessed in binary terms according to a bivalent condition — an element either belongs or does not belong to the set. By contrast, fuzzy set theory permits the gradual assessment of the membership of elements in a set; this is described with the aid of a membership function valued in the real unit interval [0, 1]. Fuzzy sets generalize classical sets, since the indicator functions (aka characteristic functions) of classical sets are special cases of the membership functions of fuzzy sets, if the latter only take values 0 or 1.[181] In fuzzy set theory, classical bivalent sets are usually called crisp sets. The fuzzy set theory can be used in a wide range of domains in which information is incomplete or imprecise, such as bioinformatics.[182]

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  • 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.[188]
  • 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.[189][190][191]

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  • Naive Bayes classifier – In machine learning, naive Bayes classifiers are a family of simple probabilistic classifiers based on applying Bayes’ theorem with strong (naive) independence assumptions between the features.
  • Naive semantics – is an approach used in computer science for representing basic knowledge about a specific domain, and has been used in applications such as the representation of the meaning of natural language sentences in artificial intelligence applications. In a general setting the term has been used to refer to the use of a limited store of generally understood knowledge about a specific domain in the world, and has been applied to fields such as the knowledge based design of data schemas.[221]
  • Name binding – In programming languages, name binding is the association of entities (data and/or code) with identifiers.[222] An identifier bound to an object is said to reference that object. Machine languages have no built-in notion of identifiers, but name-object bindings as a service and notation for the programmer is implemented by programming languages. Binding is intimately connected with scoping, as scope determines which names bind to which objects – at which locations in the program code (lexically) and in which one of the possible execution paths (temporally). Use of an identifier id in a context that establishes a binding for id is called a binding (or defining) occurrence. In all other occurrences (e.g., in expressions, assignments, and subprogram calls), an identifier stands for what it is bound to; such occurrences are called applied occurrences.
  • Named-entity recognition – (NER), (also known as entity identification, entity chunking and entity extraction) is a subtask of information extraction that seeks to locate and classify named entity mentions in unstructured text into pre-defined categories such as the person names, organizations, locations, medical codes, time expressions, quantities, monetary values, percentages, etc.
  • Named graph – Named graphs are a key concept of Semantic Web architecture in which a set of Resource Description Framework statements (a graph) are identified using a URI,[223] allowing descriptions to be made of that set of statements such as context, provenance information or other such metadata. Named graphs are a simple extension of the RDF data model[224] through which graphs can be created but the model lacks an effective means of distinguishing between them once published on the Web at large.
  • Natural language generation – (NLG), is a software process that transforms structured data into plain-English content. It can be used to produce long-form content for organizations to automate custom reports, as well as produce custom content for a web or mobile application. It can also be used to generate short blurbs of text in interactive conversations (a chatbot) which might even be read out loud by a text-to-speech system.
  • Natural language processing – (NLP), is a subfield of computer science, information engineering, and artificial intelligence concerned with the interactions between computers and human (natural) languages, in particular how to program computers to process and analyze large amounts of natural language data.
  • Natural language programming – is an ontology-assisted way of programming in terms of natural-language sentences, e.g. English.[225]
  • Network motif – All networks, including biological networks, social networks, technological networks (e.g., computer networks and electrical circuits) and more, can be represented as graphs, which include a wide variety of subgraphs. One important local property of networks are so-called network motifs, which are defined as recurrent and statistically significant sub-graphs or patterns.
  • Neural machine translation – (NMT), is an approach to machine translation that uses a large artificial neural network to predict the likelihood of a sequence of words, typically modeling entire sentences in a single integrated model.
  • Neural Turing machine – (NTMs) is a recurrent neural network model. NTMs combine the fuzzy pattern matching capabilities of neural networks with the algorithmic power of programmable computers. An NTM has a neural network controller coupled to external memory resources, which it interacts with through attentional mechanisms. The memory interactions are differentiable end-to-end, making it possible to optimize them using gradient descent.[226] An NTM with a long short-term memory (LSTM) network controller can infer simple algorithms such as copying, sorting, and associative recall from examples alone.[227]
  • Neuro-fuzzy – refers to combinations of artificial neural networks and fuzzy logic.
  • Neurocybernetics – A brain–computer interface (BCI), sometimes called a neural-control interface (NCI), mind-machine interface (MMI), direct neural interface (DNI), or brain–machine interface (BMI), is a direct communication pathway between an enhanced or wired brain and an external device. BCI differs from neuromodulation in that it allows for bidirectional information flow. BCIs are often directed at researching, mapping, assisting, augmenting, or repairing human cognitive or sensory-motor functions.[228]
  • Neuromorphic engineering – also known as neuromorphic computing,[229][230][231] is a concept describing the use of very-large-scale integration (VLSI) systems containing electronic analog circuits to mimic neuro-biological architectures present in the nervous system.[232] In recent times, the term neuromorphic has been used to describe analog, digital, mixed-mode analog/digital VLSI, and software systems that implement models of neural systems (for perception, motor control, or multisensory integration). The implementation of neuromorphic computing on the hardware level can be realized by oxide-based memristors,[233] spintronic memories,[234] threshold switches, and transistors.[235]
  • Node – is a basic unit of a data structure, such as a linked list or tree data structure. Nodes contain data and also may link to other nodes. Links between nodes are often implemented by pointers.
  • Nondeterministic algorithm – is an algorithm that, even for the same input, can exhibit different behaviors on different runs, as opposed to a deterministic algorithm.
  • Nouvelle AI – Nouvelle AI differs from classical AI by aiming to produce robots with intelligence levels similar to insects. Researchers believe that intelligence can emerge organically from simple behaviors as these intelligences interacted with the “real world,” instead of using the constructed worlds which symbolic AIs typically needed to have programmed into them.[236]
  • NP – In computational complexity theory, NP (nondeterministic polynomial time) is a complexity class used to classify decision problems. NP is the set of decision problems for which the problem instances, where the answer is “yes”, have proofs verifiable in polynomial time.[237][Note 1]
  • NP-completeness – In computational complexity theory, a problem is NP-complete when it can be solved by a restricted class of brute force search algorithms and it can be used to simulate any other problem with a similar algorithm. More precisely, each input to the problem should be associated with a set of solutions of polynomial length, whose validity can be tested quickly (in polynomial time[238]), such that the output for any input is “yes” if the solution set is non-empty and “no” if it is empty.
  • NP-hardness – (non-deterministic polynomial-time hardness), in computational complexity theory, is the defining property of a class of problems that are, informally, “at least as hard as the hardest problems in NP”. A simple example of an NP-hard problem is the subset sum problem.

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  • Partial order reduction – is a technique for reducing the size of the state-space to be searched by a model checking or automated planning and scheduling algorithm. It exploits the commutativity of concurrently executed transitions, which result in the same state when executed in different orders.
  • Partially observable Markov decision process – (POMDP), is a generalization of a Markov decision process (MDP). A POMDP models an agent decision process in which it is assumed that the system dynamics are determined by an MDP, but the agent cannot directly observe the underlying state. Instead, it must maintain a probability distribution over the set of possible states, based on a set of observations and observation probabilities, and the underlying MDP.
  • Particle swarm optimization – (PSO) is a computational method that optimizes a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality. It solves a problem by having a population of candidate solutions, here dubbed particles, and moving these particles around in the search-space according to simple mathematical formulae over the particle’s position and velocity. Each particle’s movement is influenced by its local best known position, but is also guided toward the best known positions in the search-space, which are updated as better positions are found by other particles. This is expected to move the swarm toward the best solutions.
  • Pathfinding – or pathing, is the plotting, by a computer application, of the shortest route between two points. It is a more practical variant on solving mazes. This field of research is based heavily on Dijkstra’s algorithm for finding a shortest path on a weighted graph.
  • Pattern recognition – is concerned with the automatic discovery of regularities in data through the use of computer algorithms and with the use of these regularities to take actions such as classifying the data into different categories.[244]
  • Predicate logic – First-order logic—also known as predicate logic and first-order predicate calculus—is a collection of formal systems used in mathematics, philosophy, linguistics, and computer science. First-order logic uses quantified variables over non-logical objects and allows the use of sentences that contain variables, so that rather than propositions such as Socrates is a man one can have expressions in the form “there exists x such that x is Socrates and x is a man” and there exists is a quantifier while x is a variable.[174] This distinguishes it from propositional logic, which does not use quantifiers or relations;[245] in this sense, propositional logic is the foundation of first-order logic.
  • Predictive analytics – encompasses a variety of statistical techniques from data mining, predictive modelling, and machine learning, that analyze current and historical facts to make predictions about future or otherwise unknown events.[246][247]
  • Principal component analysis – (PCA), is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables (entities each of which takes on various numerical values) into a set of values of linearly uncorrelated variables called principal components. This transformation is defined in such a way that the first principal component has the largest possible variance (that is, accounts for as much of the variability in the data as possible), and each succeeding component, in turn, has the highest variance possible under the constraint that it is orthogonal to the preceding components. The resulting vectors (each being a linear combination of the variables and containing n observations) are an uncorrelated orthogonal basis set. PCA is sensitive to the relative scaling of the original variables.
  • Principle of rationality – (or rationality principle), was coined by Karl R. Popper in his Harvard Lecture of 1963, and published in his book Myth of Framework.[248] It is related to what he called the ‘logic of the situation’ in an Economica article of 1944/1945, published later in his book The Poverty of Historicism.[249] According to Popper’s rationality principle, agents act in the most adequate way according to the objective situation. It is an idealized conception of human behavior which he used to drive his model of situational analysis.
  • Probabilistic programming – (PP), is a programming paradigm in which probabilistic models are specified and inference for these models is performed automatically.[250] It represents an attempt to unify probabilistic modeling and traditional general-purpose programming in order to make the former easier and more widely applicable.[251][252] It can be used to create systems that help make decisions in the face of uncertainty. Programming languages used for probabilistic programming are referred to as “Probabilistic programming languages” (PPLs).
  • Production system –
  • Programming language – is a formal language, which comprises a set of instructions that produce various kinds of output. Programming languages are used in computer programming to implement algorithms.
  • Prolog – is a logic programming language associated with artificial intelligence and computational linguistics.[253][254][255] Prolog has its roots in first-order logic, a formal logic, and unlike many other programming languages, Prolog is intended primarily as a declarative programming language: the program logic is expressed in terms of relations, represented as facts and rules. A computation is initiated by running a query over these relations.[256]
  • Propositional calculus – is a branch of logic. It is also called propositional logic, statement logic, sentential calculus, sentential logic, or sometimes zeroth-order logic. It deals with propositions (which can be true or false) and argument flow. Compound propositions are formed by connecting propositions by logical connectives. The propositions without logical connectives are called atomic propositions. Unlike first-order logic, propositional logic does not deal with non-logical objects, predicates about them, or quantifiers. However, all the machinery of propositional logic is included in first-order logic and higher-order logics. In this sense, propositional logic is the foundation of first-order logic and higher-order logic.
  • Python – is an interpreted, high-level, general-purpose programming language. Created by Guido van Rossum and first released in 1991, Python’s design philosophy emphasizes code readability with its notable use of significant whitespace. Its language constructs and object-oriented approach aim to help programmers write clear, logical code for small and large-scale projects.[257]

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See also

References

  1. ^ Jump up to:a b For example: Josephson, John R.; Josephson, Susan G., eds. (1994). Abductive Inference: Computation, Philosophy, Technology. Cambridge, UK; New York: Cambridge University Press. doi:10.1017/CBO9780511530128. ISBN 978-0521434614. OCLC 28149683.
  2. ^ “Retroduction | Dictionary | Commens”. Commens – Digital Companion to C. S. Peirce. Mats Bergman, Sami Paavola & João Queiroz. Retrieved 24 August 2014.
  3. ^ Colburn, Timothy; Shute, Gary (5 June 2007). “Abstraction in Computer Science”. Minds and Machines. 17 (2): 169–184. doi:10.1007/s11023-007-9061-7. ISSN 0924-6495.
  4. ^ Kramer, Jeff (1 April 2007). “Is abstraction the key to computing?”. Communications of the ACM. 50 (4): 36–42. CiteSeerX 10.1.1.120.6776. doi:10.1145/1232743.1232745. ISSN 0001-0782.
  5. ^ Michael Gelfond, Vladimir Lifschitz (1998) “Action Languages“, Linköping Electronic Articles in Computer and Information Science, vol 3, nr 16.
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Notes

  1. ^ polynomial time refers to how quickly the number of operations needed by an algorithm, relative to the size of the problem, grows. It is therefore a measure of efficiency of an algorithm.

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