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| title | chunk | source | category | tags | date_saved | instance |
|---|---|---|---|---|---|---|
| Glossary of artificial intelligence | 10/21 | https://en.wikipedia.org/wiki/Glossary_of_artificial_intelligence | reference | science, encyclopedia | 2026-05-05T07:50:25.401446+00:00 | kb-cron |
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 The problem of finding adequate collections of axioms for a viable description of a robot environment.
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 The study of postulating possible, probable, and preferable futures and the worldviews and myths that underlie them.
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).
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 A rule 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. 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.
== G ==
game theory The study of mathematical models of strategic interaction between rational decision-makers.
general game playing (GGP) General game playing is the design of artificial intelligence programs to be able to run and play more than one game successfully.
generalization The concept that humans, other animals, and artificial neural networks use past learning in present situations of learning if the conditions in the situations are regarded as similar.
generalization error For supervised learning applications in machine learning and statistical learning theory, generalization error (also known as the out-of-sample error or the risk) is a measure of how accurately a learning algorithm is able to predict outcomes for previously unseen data.
generative adversarial network (GAN) A class of machine learning systems. Two neural networks contest with each other in a zero-sum game framework.
generative artificial intelligence Generative artificial intelligence is artificial intelligence capable of generating text, images, or other media in response to prompts. Generative AI models learn the patterns and structure of their input training data and then generate new data that has similar characteristics, typically using transformer-based deep neural networks.
generative pretrained transformer (GPT) A large language model based on the transformer architecture that generates text. It is first pretrained to predict the next token in texts (a token is typically a word, subword, or punctuation). After their pretraining, GPT models can generate human-like text by repeatedly predicting the token that they would expect to follow. GPT models are usually also fine-tuned, for example with reinforcement learning from human feedback to reduce hallucination or harmful behaviour, or to format the output in a conversationnal format.
genetic algorithm (GA) A metaheuristic inspired by the process of natural selection that belongs to the larger class of evolutionary algorithms (EA). Genetic algorithms are commonly used to generate high-quality solutions to optimization and search problems by relying on bio-inspired operators such as mutation, crossover and selection.
genetic operator An operator used in genetic algorithms to guide the algorithm towards a solution to a given problem. There are three main types of operators (mutation, crossover and selection), which must work in conjunction with one another in order for the algorithm to be successful.
glowworm swarm optimization A swarm intelligence optimization algorithm based on the behaviour of glowworms (also known as fireflies or lightning bugs).
gradient boosting A machine learning technique based on boosting in a functional space, where the target is pseudo-residuals instead of residuals as in traditional boosting.
graph (abstract data type) In computer science, a graph is an abstract data type that is meant to implement the undirected graph and directed graph concepts from mathematics; specifically, the field of graph theory.
graph (discrete mathematics) In mathematics, and more specifically in graph theory, a graph is a structure amounting to a set of objects in which some pairs of the objects are in some sense "related". The objects correspond to mathematical abstractions called vertices (also called nodes or points) and each of the related pairs of vertices is called an edge (also called an arc or line).