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GOFAI
GOFAI is an acronym for "Good Old-Fashioned Artificial Intelligence" invented by the philosopher John Haugeland in his 1985 book, ''Artificial Intelligence: The Very Idea''. Technically, GOFAI refers only to a restricted kind of symbolic AI, namely rule-based or logical agents. This approach was popular in the 1980s, especially as an approach to implementing expert systems, but symbolic AI has since been extended in many ways to better handle uncertain reasoning and more open-ended systems. Some of these extensions include probabilistic reasoning, non-monotonic reasoning, multi-agent systems, and neuro-symbolic systems. Significant contributions of symbolic AI, not encompassed by the GOFAI view, include search algorithms; automated planning and scheduling; constraint-based reasoning; the semantic web; ontologies; knowledge graphs; non-monotonic logic; circumscription; automated theorem proving; and symbolic mathematics. For a more complete list, see the main article on symbo ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Symbolic AI
In artificial intelligence, symbolic artificial intelligence is the term for the collection of all methods in artificial intelligence research that are based on high-level symbolic (human-readable) representations of problems, logic and search. Symbolic AI used tools such as logic programming, production rules, semantic nets and frames, and it developed applications such as knowledge-based systems (in particular, expert systems), symbolic mathematics, automated theorem provers, ontologies, the semantic web, and automated planning and scheduling systems. The Symbolic AI paradigm led to seminal ideas in search, symbolic programming languages, agents, multi-agent systems, the semantic web, and the strengths and limitations of formal knowledge and reasoning systems. Symbolic AI was the dominant paradigm of AI research from the mid-1950s until the middle 1990s. Researchers in the 1960s and the 1970s were convinced that symbolic approaches would eventually succeed in creating ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Symbolic Artificial Intelligence
In artificial intelligence, symbolic artificial intelligence is the term for the collection of all methods in artificial intelligence research that are based on high-level symbolic (human-readable) representations of problems, logic and search. Symbolic AI used tools such as logic programming, production rules, semantic nets and frames, and it developed applications such as knowledge-based systems (in particular, expert systems), symbolic mathematics, automated theorem provers, ontologies, the semantic web, and automated planning and scheduling systems. The Symbolic AI paradigm led to seminal ideas in search, symbolic programming languages, agents, multi-agent systems, the semantic web, and the strengths and limitations of formal knowledge and reasoning systems. Symbolic AI was the dominant paradigm of AI research from the mid-1950s until the middle 1990s. Researchers in the 1960s and the 1970s were convinced that symbolic approaches would eventually succeed in creating ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Symbolic Artificial Intelligence
In artificial intelligence, symbolic artificial intelligence is the term for the collection of all methods in artificial intelligence research that are based on high-level symbolic (human-readable) representations of problems, logic and search. Symbolic AI used tools such as logic programming, production rules, semantic nets and frames, and it developed applications such as knowledge-based systems (in particular, expert systems), symbolic mathematics, automated theorem provers, ontologies, the semantic web, and automated planning and scheduling systems. The Symbolic AI paradigm led to seminal ideas in search, symbolic programming languages, agents, multi-agent systems, the semantic web, and the strengths and limitations of formal knowledge and reasoning systems. Symbolic AI was the dominant paradigm of AI research from the mid-1950s until the middle 1990s. Researchers in the 1960s and the 1970s were convinced that symbolic approaches would eventually succeed in creating ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
John Haugeland
John Haugeland (; March 13, 1945 – June 23, 2010) was a professor of philosophy, specializing in the philosophy of mind, cognitive science, phenomenology, and Heidegger. He spent most of his career at the University of Pittsburgh, followed by the University of Chicago from 1999 until his death. He is featured in Tao Ruspoli's film ''Being in the World''. Education and career Haugeland studied at Harvey Mudd College, where he obtained a BS cum laude in physics in 1966. He received a PhD in philosophy at the University of California, Berkeley, completing his dissertation, entitled ''Truth and Understanding'', under the supervision of Hans Sluga in 1976. At Berkeley, Hubert Dreyfus served as one of his important mentors, becoming almost a ''de facto'' doctoral advisor. Haugeland spent most of his career teaching at the University of Pittsburgh, from 1974 until 1999, and he also served as a visiting professor at Helsinki University, Finland. He served as chair of the phi ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Tom M
Tom or TOM may refer to: * Tom (given name), a diminutive of Thomas or Tomás or an independent Aramaic given name (and a list of people with the name) Characters * Tom Anderson, a character in ''Beavis and Butt-Head'' * Tom Beck, a character in the 1998 American science-fiction disaster movie '' Deep Impact'' * Tom Buchanan, the main antagonist from the 1925 novel ''The Great Gatsby'' * Tom Cat, a character from the ''Tom and Jerry'' cartoons * Tom Lucitor, a character from the American animated series ''Star vs. the Forces of Evil'' * Tom Natsworthy, from the science fantasy novel ''Mortal Engines'' * Tom Nook, a character in ''Animal Crossing'' video game series * Tom Servo, a robot character from the ''Mystery Science Theater 3000'' television series * Tom Sloane, a non-adult character from the animated sitcom ''Daria'' * Talking Tom, the protagonist from the ''Talking Tom & Friends'' franchise * Tom, a character from the '' Deltora Quest'' books by Emily Rodda * Tom, a cha ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Decision Tree Learning
Decision tree learning is a supervised learning approach used in statistics, data mining and machine learning. In this formalism, a classification or regression decision tree is used as a predictive model to draw conclusions about a set of observations. Tree models where the target variable can take a discrete set of values are called classification trees; in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. Decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. Decision trees are among the most popular machine learning algorithms given their intelligibility and simplicity. In decision analysis, a decision tree can be used to visually and explicitly represent decisions and decision making. In data mining, a decision tree describes data (but the resulting classification tree can be an input for decision making). General Dec ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
YAGO (database)
YAGO (Yet Another Great Ontology) is an open source knowledge base developed at the Max Planck Institute for Computer Science in Saarbrücken. It is automatically extracted from Wikipedia and other sources. As of 2019, YAGO3 has knowledge of more than 10 million entities and contains more than 120 million facts about these entities. The information in YAGO is extracted from Wikipedia (e.g., categories, redirects, infoboxes), WordNet (e.g., synsets, hyponymy), and GeoNames. The accuracy of YAGO was manually evaluated to be above 95% on a sample of facts. To integrate it to the linked data cloud, YAGO has been linked to the DBpedia ontology and to the SUMO ontology. YAGO3 is provided in Turtle and tsv formats. Dumps of the whole database are available, as well as thematic and specialized dumps. It can also be queried through various online browsers and through a SPARQL endpoint hosted by OpenLink Software. The source code of YAGO3 is available on GitHub. YAGO has been used in th ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Web Ontology Language
The Web Ontology Language (OWL) is a family of knowledge representation languages for authoring ontologies. Ontologies are a formal way to describe taxonomies and classification networks, essentially defining the structure of knowledge for various domains: the nouns representing classes of objects and the verbs representing relations between the objects. Ontologies resemble class hierarchies in object-oriented programming but there are several critical differences. Class hierarchies are meant to represent structures used in source code that evolve fairly slowly (perhaps with monthly revisions) whereas ontologies are meant to represent information on the Internet and are expected to be evolving almost constantly. Similarly, ontologies are typically far more flexible as they are meant to represent information on the Internet coming from all sorts of heterogeneous data sources. Class hierarchies on the other hand tend to be fairly static and rely on far less diverse and more structu ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Resource Description Framework
The Resource Description Framework (RDF) is a World Wide Web Consortium (W3C) standard originally designed as a data model for metadata. It has come to be used as a general method for description and exchange of graph data. RDF provides a variety of syntax notations and data serialization formats with Turtle (Terse RDF Triple Language) currently being the most widely used notation. RDF is a directed graph composed of triple statements. An RDF graph statement is represented by: 1) a node for the subject, 2) an arc that goes from a subject to an object for the predicate, and 3) a node for the object. Each of the three parts of the statement can be identified by a URI. An object can also be a literal value. This simple, flexible data model has a lot of expressive power to represent complex situations, relationships, and other things of interest, while also being appropriately abstract. RDF was adopted as a W3C recommendation in 1999. The RDF 1.0 specification was published in 2004, th ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Knowledge Representation And Reasoning
Knowledge representation and reasoning (KRR, KR&R, KR²) is the field of artificial intelligence (AI) dedicated to representing information about the world in a form that a computer system can use to solve complex tasks such as Computer-aided diagnosis, diagnosing a medical condition or natural language user interface, having a dialog in a natural language. Knowledge representation incorporates findings from psychology about how humans solve problems and represent knowledge in order to design Formalism (mathematics), formalisms that will make complex systems easier to design and build. Knowledge representation and reasoning also incorporates findings from logic to automate various kinds of ''reasoning'', such as the application of rules or the relations of Set theory, sets and subsets. Examples of knowledge representation formalisms include Semantic network, semantic nets, systems architecture, Frame (artificial intelligence), frames, rules, and Ontology (information science), ont ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Semantic Network
A semantic network, or frame network is a knowledge base that represents semantic relations between concepts in a network. This is often used as a form of knowledge representation. It is a directed or undirected graph consisting of vertices, which represent concepts, and edges, which represent semantic relations between concepts, mapping or connecting semantic fields. A semantic network may be instantiated as, for example, a graph database or a concept map. Typical standardized semantic networks are expressed as semantic triples. Semantic networks are used in natural language processing applications such as semantic parsing and word-sense disambiguation. Semantic networks can also be used as a method to analyze large texts and identify the main themes and topics (e.g., of social media posts), to reveal biases (e.g., in news coverage), or even to map an entire research field. History Examples of the use of semantic networks in logic, directed acyclic graphs as a mnemonic to ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Commonsense 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 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 properties." * "Commonsense knowledge differs from e ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
