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Knowledge Representation And Reasoning
Knowledge representation (KR) aims to model information in a structured manner to formally represent it as knowledge in knowledge-based systems whereas knowledge representation and reasoning (KRR, KR&R, or KR²) also aims to understand, reason, and interpret knowledge. KRR is widely used in the field of artificial intelligence (AI) with the goal to represent information about the world in a form that a computer system can use to solve complex tasks, such as diagnosing a medical condition or having a natural-language dialog. KR incorporates findings from psychology about how humans solve problems and represent knowledge, in order to design formalisms that make complex systems easier to design and build. KRR also incorporates findings from logic to automate various kinds of ''reasoning''. Traditional KRR focuses more on the declarative representation of knowledge. Related knowledge representation formalisms mainly include vocabularies, thesaurus, semantic networks, axiom system ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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Artificial Intelligence
Artificial intelligence (AI) is the capability of computer, computational systems to perform tasks typically associated with human intelligence, such as learning, reasoning, problem-solving, perception, and decision-making. It is a field of research in computer science that develops and studies methods and software that enable machines to machine perception, perceive their environment and use machine learning, learning and intelligence to take actions that maximize their chances of achieving defined goals. High-profile applications of AI include advanced web search engines (e.g., Google Search); recommendation systems (used by YouTube, Amazon (company), Amazon, and Netflix); virtual assistants (e.g., Google Assistant, Siri, and Amazon Alexa, Alexa); autonomous vehicles (e.g., Waymo); Generative artificial intelligence, generative and Computational creativity, creative tools (e.g., ChatGPT and AI art); and Superintelligence, superhuman play and analysis in strategy games (e.g., ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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Boolean Satisfiability Problem
In logic and computer science, the Boolean satisfiability problem (sometimes called propositional satisfiability problem and abbreviated SATISFIABILITY, SAT or B-SAT) asks whether there exists an Interpretation (logic), interpretation that Satisfiability, satisfies a given Boolean logic, Boolean Formula (mathematical logic), formula. In other words, it asks whether the formula's variables can be consistently replaced by the values TRUE or FALSE to make the formula evaluate to TRUE. If this is the case, the formula is called ''satisfiable'', else ''unsatisfiable''. For example, the formula "''a'' AND NOT ''b''" is satisfiable because one can find the values ''a'' = TRUE and ''b'' = FALSE, which make (''a'' AND NOT ''b'') = TRUE. In contrast, "''a'' AND NOT ''a''" is unsatisfiable. SAT is the first problem that was proven to be NP-complete—this is the Cook–Levin theorem. This means that all problems in the complexity class NP (complexity), NP, which includes a wide range of natu ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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Common Sense Reasoning
In artificial intelligence (AI), commonsense reasoning is a human-like ability to make presumptions about the type and essence of ordinary situations humans encounter every day. These assumptions include judgments about the nature of physical objects, taxonomic properties, and peoples' intentions. A device that exhibits commonsense reasoning might be capable of drawing conclusions that are similar to humans' folk psychology (humans' innate ability to reason about people's behavior and intentions) and naive physics (humans' natural understanding of the physical world). Definitions and characterizations Some definitions and characterizations of common sense from different authors include: * "Commonsense knowledge (artificial intelligence), Commonsense knowledge includes the basic facts about events (including actions) and their effects, facts about knowledge and how it is obtained, facts about beliefs and desires. It also includes the basic facts about material objects and their pro ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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Predicate Calculus
{{disambiguation ...
Predicate or predication may refer to: * Predicate (grammar), in linguistics * Predication (philosophy) * several closely related uses in mathematics and formal logic: **Predicate (mathematical logic) ** Propositional function **Finitary relation, or n-ary predicate ** Boolean-valued function ** Syntactic predicate, in formal grammars and parsers **Functional predicate * Predication (computer architecture) *in United States law, the basis or foundation of something ** Predicate crime **Predicate rules, in the U.S. Title 21 CFR Part 11 * Predicate, a term used in some European context for either nobles' honorifics or for nobiliary particles See also * Predicate logic First-order logic, also called predicate logic, predicate calculus, or quantificational logic, is a collection of formal systems used in mathematics, philosophy, linguistics, and computer science. First-order logic uses quantified variables ove ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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John McCarthy (computer Scientist)
John McCarthy (September 4, 1927 – October 24, 2011) was an American computer scientist and cognitive scientist. He was one of the founders of the discipline of artificial intelligence. He co-authored the document that coined the term "artificial intelligence" (AI), developed the programming language family Lisp (programming language), Lisp, significantly influenced the design of the language ALGOL, popularized time-sharing, and invented Garbage collection (computer science), garbage collection. McCarthy spent most of his career at Stanford University. He received many accolades and honors, such as the 1971 Turing Award for his contributions to the topic of AI, the United States National Medal of Science, and the Kyoto Prize. Early life and education John McCarthy was born in Boston, Massachusetts, on September 4, 1927, to an Irish people, Irish immigrant father and a Lithuanian Jewish immigrant mother, John Patrick and Ida (Glatt) McCarthy. The family was obliged to relocat ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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Herbert A
Herbert may refer to: People * Herbert (musician), a pseudonym of Matthew Herbert * Herbert (given name) * Herbert (surname) Places Antarctica * Herbert Mountains, Coats Land * Herbert Sound, Graham Land Australia * Herbert, Northern Territory, a rural locality * Herbert, South Australia. former government town * Division of Herbert, an electoral district in Queensland * Herbert River, a river in Queensland * County of Herbert, a cadastral unit in South Australia Canada * Herbert, Saskatchewan, Canada, a town * Herbert Road, St. Albert, Canada New Zealand * Herbert, New Zealand, a town * Mount Herbert (New Zealand) United States * Herbert, Illinois, an unincorporated community * Herbert, Michigan, a former settlement * Herbert Creek, a stream in South Dakota * Herbert Island, Alaska Arts, entertainment, and media Fictional entities * Herbert (Disney character) * Herbert Pocket, a character in the Charles Dickens novel ''Great Expectations'' * Herbert West ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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Allen Newell
Allen Newell (March 19, 1927 – July 19, 1992) was an American researcher in computer science and cognitive psychology at the RAND Corporation and at Carnegie Mellon University's School of Computer Science, Tepper School of Business, and Department of Psychology. He, Herbert A. Simon, and Cliff Shaw contributed to the Information Processing Language (1956) and two of the earliest AI programs, the Logic Theorist (1956) and the General Problem Solver (1957). He and Simon were awarded the ACM's A.M. Turing Award in 1975 for their contributions to artificial intelligence and the psychology of human cognition. Early studies Newell completed his bachelor's degree in physics from Stanford in 1949. He was a graduate student at Princeton University from 1949 to 1950, where he studied mathematics. Due to his early exposure to an unknown field known as game theory and the experiences from the study of mathematics, he was convinced that he would prefer a combination of experime ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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General Problem Solver
General Problem Solver (GPS) is a computer program created in 1957 by Herbert A. Simon, J. C. Shaw, and Allen Newell ( RAND Corporation) intended to work as a universal problem solver machine. In contrast to the former Logic Theorist project, the GPS works with means–ends analysis. Overview Any problem that can be expressed as a set of well-formed formulas (WFFs) or Horn clauses, and that constitutes a directed graph with one or more sources (that is, hypotheses) and sinks (that is, desired conclusions), can be solved, in principle, by GPS. Proofs in the predicate logic and Euclidean geometry problem spaces are prime examples of the domain of applicability of GPS. It was based on Simon and Newell's theoretical work on logic machines. GPS was the first computer program that separated its knowledge of problems (rules represented as input data) from its strategy of how to solve problems (a generic solver engine). GPS was implemented in the third-order programming language ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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Artificial General Intelligence
Artificial general intelligence (AGI)—sometimes called human‑level intelligence AI—is a type of artificial intelligence that would match or surpass human capabilities across virtually all cognitive tasks. Some researchers argue that state‑of‑the‑art large language models already exhibit early signs of AGI‑level capability, while others maintain that genuine AGI has not yet been achieved. AGI is conceptually distinct from artificial superintelligence (ASI), which would outperform the best human abilities across every domain by a wide margin. AGI is considered one of the definitions of Chinese room#Strong AI vs. AI research, strong AI. Unlike artificial narrow intelligence (ANI), whose competence is confined to well‑defined tasks, an AGI system can generalise knowledge, transfer skills between domains, and solve novel problems without task‑specific reprogramming. The concept does not, in principle, require the system to be an autonomous agent; a static model— ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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Transformer (deep Learning Architecture)
The transformer is a deep learning architecture based on the multi-head attention mechanism, in which text is converted to numerical representations called tokens, and each token is converted into a vector via lookup from a word embedding table. At each layer, each token is then contextualized within the scope of the context window with other (unmasked) tokens via a parallel multi-head attention mechanism, allowing the signal for key tokens to be amplified and less important tokens to be diminished. Transformers have the advantage of having no recurrent units, therefore requiring less training time than earlier recurrent neural architectures (RNNs) such as long short-term memory (LSTM). Later variations have been widely adopted for training large language models (LLM) on large (language) datasets. The modern version of the transformer was proposed in the 2017 paper " Attention Is All You Need" by researchers at Google. Transformers were first developed as an improvement ov ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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Convolutional Neural Network
A convolutional neural network (CNN) is a type of feedforward neural network that learns features via filter (or kernel) optimization. This type of deep learning network has been applied to process and make predictions from many different types of data including text, images and audio. Convolution-based networks are the de-facto standard in deep learning-based approaches to computer vision and image processing, and have only recently been replaced—in some cases—by newer deep learning architectures such as the transformer. Vanishing gradients and exploding gradients, seen during backpropagation in earlier neural networks, are prevented by the regularization that comes from using shared weights over fewer connections. For example, for ''each'' neuron in the fully-connected layer, 10,000 weights would be required for processing an image sized 100 × 100 pixels. However, applying cascaded ''convolution'' (or cross-correlation) kernels, only 25 weights for each convolutio ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |