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| Glossary of artificial intelligence | 17/21 | https://en.wikipedia.org/wiki/Glossary_of_artificial_intelligence | reference | science, encyclopedia | 2026-05-05T07:50:25.401446+00:00 | kb-cron |
propositional calculus Also propositional logic, statement logic, sentential calculus, sentential logic, and zeroth-order logic.A branch of logic which 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. proximal policy optimization (PPO) A reinforcement learning algorithm for training an intelligent agent's decision function to accomplish difficult tasks.
Python 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.
PyTorch A machine learning library based on the Torch library, used for applications such as computer vision and natural language processing, originally developed by Meta AI and now part of the Linux Foundation umbrella.
== Q ==
Q-learning A model-free reinforcement learning algorithm for learning the value of an action in a particular state.
qualification problem In philosophy and artificial intelligence (especially knowledge-based systems), the qualification problem is concerned with the impossibility of listing all of the preconditions required for a real-world action to have its intended effect. It might be posed as how to deal with the things that prevent me from achieving my intended result. It is strongly connected to, and opposite the ramification side of, the frame problem.
quantifier In logic, quantification specifies the quantity of specimens in the domain of discourse that satisfy an open formula. The two most common quantifiers mean "for all" and "there exists". For example, in arithmetic, quantifiers allow one to say that the natural numbers go on forever, by writing that for all n (where n is a natural number), there is another number (say, the successor of n) which is one bigger than n.
quantum computing The use of quantum-mechanical phenomena such as superposition and entanglement to perform computation. A quantum computer is used to perform such computation, which can be implemented theoretically or physically.
query language Query languages or data query languages (DQLs) are computer languages used to make queries in databases and information systems. Broadly, query languages can be classified according to whether they are database query languages or information retrieval query languages. The difference is that a database query language attempts to give factual answers to factual questions, while an information retrieval query language attempts to find documents containing information that is relevant to an area of inquiry.
== R ==
R programming language A programming language and free software environment for statistical computing and graphics supported by the R Foundation for Statistical Computing. The R language is widely used among statisticians and data miners for developing statistical software and data analysis.
radial basis function network In the field of mathematical modeling, a radial basis function network is an artificial neural network that uses radial basis functions as activation functions. The output of the network is a linear combination of radial basis functions of the inputs and neuron parameters. Radial basis function networks have many uses, including function approximation, time series prediction, classification, and system control. They were first formulated in a 1988 paper by Broomhead and Lowe, both researchers at the Royal Signals and Radar Establishment.
random forest Also random decision forest.An ensemble learning method for classification, regression, and other tasks that operates by constructing a multitude of decision trees at training time and outputting the class that is the mode of the classes (classification) or mean prediction (regression) of the individual trees. Random decision forests correct for decision trees' habit of overfitting to their training set. reasoning engine Another name for semantic reasoner.
reasoning model Also known as a reasoning language model (RLM) or large reasoning model (LRM). A type of large language model (LLM) that has been specifically trained to solve complex tasks requiring multiple steps of logical reasoning. reasoning system In information technology a reasoning system is a software system that generates conclusions from available knowledge using logical techniques such as deduction and induction. Reasoning systems play an important role in the implementation of artificial intelligence and knowledge-based systems.
recurrent neural network (RNN) A class of artificial neural networks where connections between nodes form a directed graph along a temporal sequence. This allows it to exhibit temporal dynamic behavior. Unlike feedforward neural networks, RNNs can use their internal state (memory) to process sequences of inputs. This makes them applicable to tasks such as unsegmented, connected handwriting recognition or speech recognition.
Retrieval-Augmented Generation (RAG) A technique in natural language processing that combines a retrieval system with a generative language model. Rather than relying solely on knowledge encoded during training, a RAG system first retrieves relevant documents or passages from an external knowledge base in response to a query, then passes this retrieved context to the generative model to produce a more accurate and up-to-date response. RAG is commonly used to reduce hallucination in large language models and to enable models to answer questions about information not present in their training data.
recursive self-improvement Process in which early artificial general intelligence (AGI) systems rewrite their own computer code, causing an intelligence explosion resulting from enhancing their own capabilities and intellectual capacity, theoretically resulting in superintelligence.
regression analysis
A set of statistical processes for estimating the relationships between a dependent variable (often called the outcome or response variable, or label in machine learning) and one or more error-free independent variables (often called regressors, predictors, covariates, explanatory variables, or features). The most common form of regression analysis is linear regression, in which one finds the line (or a more complex linear combination) that most closely fits the data according to a specific mathematical criterion.