70-411: DQL may refer to: DAML+OIL Query Language, an RDF query language . Data query language , particularly for SQL . Doctrine Query Language, for Doctrine (PHP) . Topics referred to by the same term [REDACTED] This disambiguation page lists articles associated with the title DQL . If an internal link led you here, you may wish to change
140-581: A loss function . Variants of gradient descent are commonly used to train neural networks. Another type of local search is evolutionary computation , which aims to iteratively improve a set of candidate solutions by "mutating" and "recombining" them, selecting only the fittest to survive each generation. Distributed search processes can coordinate via swarm intelligence algorithms. Two popular swarm algorithms used in search are particle swarm optimization (inspired by bird flocking ) and ant colony optimization (inspired by ant trails ). Formal logic
210-472: A "degree of truth" between 0 and 1. It can therefore handle propositions that are vague and partially true. Non-monotonic logics , including logic programming with negation as failure , are designed to handle default reasoning . Other specialized versions of logic have been developed to describe many complex domains. Many problems in AI (including in reasoning, planning, learning, perception, and robotics) require
280-661: A Candidate Recommendation in March 2000. In February 2001, the Semantic Web Activity replaced the Metadata Activity. In 2004 (as part of a wider revision of RDF) RDFS became a W3C Recommendation. Though RDFS provides some support for ontology specification, the need for a more expressive ontology language had become clear. As of Monday, the 31st of May, our working group will officially come to an end. We have achieved all that we were chartered to do, and I believe our work
350-412: A class may be a subclass of many classes, a class cannot be an instance of another class). OWL DL is so named due to its correspondence with description logic , a field of research that has studied the logics that form the formal foundation of OWL. This one can be expressed as S H O I N ( D ) {\displaystyle {\mathcal {SHOIN}}(\mathbf {D} )} , using
420-544: A common framework that allows data to be shared and reused across application, enterprise, and community boundaries. a declarative representation language influenced by ideas from knowledge representation In the late 1990s, the World Wide Web Consortium (W3C) Metadata Activity started work on RDF Schema (RDFS), a language for RDF vocabulary sharing. The RDF became a W3C Recommendation in February 1999, and RDFS
490-460: A contradiction from premises that include the negation of the problem to be solved. Inference in both Horn clause logic and first-order logic is undecidable , and therefore intractable . However, backward reasoning with Horn clauses, which underpins computation in the logic programming language Prolog , is Turing complete . Moreover, its efficiency is competitive with computation in other symbolic programming languages. Fuzzy logic assigns
560-409: A pair of individual identifiers (that the objects identified are distinct or the same). Axioms specify the characteristics of classes and properties. This style is similar to frame languages , and quite dissimilar to well known syntaxes for DLs and Resource Description Framework (RDF). Sean Bechhofer, et al. argue that though this syntax is hard to parse, it is quite concrete. They conclude that
630-429: A path to a target goal, a process called means-ends analysis . Simple exhaustive searches are rarely sufficient for most real-world problems: the search space (the number of places to search) quickly grows to astronomical numbers . The result is a search that is too slow or never completes. " Heuristics " or "rules of thumb" can help prioritize choices that are more likely to reach a goal. Adversarial search
700-400: A subset of first-order logic that is decidable, propositional logic was used, increasing its power by adding logics represented by convention with acronyms: The W3C-endorsed OWL specification includes the definition of three variants of OWL, with different levels of expressiveness. These are OWL Lite, OWL DL and OWL Full (ordered by increasing expressiveness). Each of these sublanguages is
770-564: A syntactic extension of its simpler predecessor. The following set of relations hold. Their inverses do not. OWL Lite was originally intended to support those users primarily needing a classification hierarchy and simple constraints. For example, while it supports cardinality constraints, it only permits cardinality values of 0 or 1. It was hoped that it would be simpler to provide tool support for OWL Lite than its more expressive relatives, allowing quick migration path for systems using thesauri and other taxonomies . In practice, however, most of
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#1732883576898840-570: A syntax for describing and exchanging ontologies, and formal semantics that gives them meaning. For example, OWL DL corresponds to the S H O I N ( D ) {\displaystyle {\mathcal {SHOIN}}^{\mathcal {(D)}}} description logic, while OWL 2 corresponds to the S R O I Q ( D ) {\displaystyle {\mathcal {SROIQ}}^{\mathcal {(D)}}} logic. Sound, complete, terminating reasoners (i.e. systems which are guaranteed to derive every consequence of
910-721: A tool that can be used for reasoning (using the Bayesian inference algorithm), learning (using the expectation–maximization algorithm ), planning (using decision networks ) and perception (using dynamic Bayesian networks ). Probabilistic algorithms can also be used for filtering, prediction, smoothing, and finding explanations for streams of data, thus helping perception systems analyze processes that occur over time (e.g., hidden Markov models or Kalman filters ). The simplest AI applications can be divided into two types: classifiers (e.g., "if shiny then diamond"), on one hand, and controllers (e.g., "if diamond then pick up"), on
980-669: A wide range of techniques, including search and mathematical optimization , formal logic , artificial neural networks , and methods based on statistics , operations research , and economics . AI also draws upon psychology , linguistics , philosophy , neuroscience , and other fields. Artificial intelligence was founded as an academic discipline in 1956, and the field went through multiple cycles of optimism, followed by periods of disappointment and loss of funding, known as AI winter . Funding and interest vastly increased after 2012 when deep learning outperformed previous AI techniques. This growth accelerated further after 2017 with
1050-487: A wide variety of techniques to accomplish the goals above. AI can solve many problems by intelligently searching through many possible solutions. There are two very different kinds of search used in AI: state space search and local search . State space search searches through a tree of possible states to try to find a goal state. For example, planning algorithms search through trees of goals and subgoals, attempting to find
1120-1139: Is intelligence exhibited by machines , particularly computer systems . It is a field of research in computer science that develops and studies methods and software that enable machines to perceive their environment and use learning and intelligence to take actions that maximize their chances of achieving defined goals. Such machines may be called AIs. Some high-profile applications of AI include advanced web search engines (e.g., Google Search ); recommendation systems (used by YouTube , Amazon , and Netflix ); interacting via human speech (e.g., Google Assistant , Siri , and Alexa ); autonomous vehicles (e.g., Waymo ); generative and creative tools (e.g., ChatGPT , and AI art ); and superhuman play and analysis in strategy games (e.g., chess and Go ). However, many AI applications are not perceived as AI: "A lot of cutting edge AI has filtered into general applications, often without being called AI because once something becomes useful enough and common enough it's not labeled AI anymore ." The various subfields of AI research are centered around particular goals and
1190-641: Is a body of knowledge represented in a form that can be used by a program. An ontology is the set of objects, relations, concepts, and properties used by a particular domain of knowledge. Knowledge bases need to represent things such as objects, properties, categories, and relations between objects; situations, events, states, and time; causes and effects; knowledge about knowledge (what we know about what other people know); default reasoning (things that humans assume are true until they are told differently and will remain true even when other facts are changing); and many other aspects and domains of knowledge. Among
1260-637: 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
1330-459: Is an input, at least one hidden layer of nodes and an output. Each node applies a function and once the weight crosses its specified threshold, the data is transmitted to the next layer. A network is typically called a deep neural network if it has at least 2 hidden layers. Learning algorithms for neural networks use local search to choose the weights that will get the right output for each input during training. The most common training technique
1400-462: Is an interdisciplinary umbrella that comprises systems that recognize, interpret, process, or simulate human feeling, emotion, and mood . For example, some virtual assistants are programmed to speak conversationally or even to banter humorously; it makes them appear more sensitive to the emotional dynamics of human interaction, or to otherwise facilitate human–computer interaction . However, this tends to give naïve users an unrealistic conception of
1470-444: Is an unsolved problem. Knowledge representation and knowledge engineering allow AI programs to answer questions intelligently and make deductions about real-world facts. Formal knowledge representations are used in content-based indexing and retrieval, scene interpretation, clinical decision support, knowledge discovery (mining "interesting" and actionable inferences from large databases ), and other areas. A knowledge base
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#17328835768981540-422: Is anything that perceives and takes actions in the world. A rational agent has goals or preferences and takes actions to make them happen. In automated planning , the agent has a specific goal. In automated decision-making , the agent has preferences—there are some situations it would prefer to be in, and some situations it is trying to avoid. The decision-making agent assigns a number to each situation (called
1610-519: Is being quite well appreciated. The World Wide Web Consortium (W3C) created the Web-Ontology Working Group as part of their Semantic Web Activity. It began work on November 1, 2001 with co-chairs James Hendler and Guus Schreiber. The first working drafts of the abstract syntax , reference and synopsis were published in July 2002. OWL became a formal W3C recommendation on February 10, 2004 and
1680-413: Is classified based on previous experience. There are many kinds of classifiers in use. The decision tree is the simplest and most widely used symbolic machine learning algorithm. K-nearest neighbor algorithm was the most widely used analogical AI until the mid-1990s, and Kernel methods such as the support vector machine (SVM) displaced k-nearest neighbor in the 1990s. The naive Bayes classifier
1750-413: Is labelled by a solution of the problem and whose leaf nodes are labelled by premises or axioms . In the case of Horn clauses , problem-solving search can be performed by reasoning forwards from the premises or backwards from the problem. In the more general case of the clausal form of first-order logic , resolution is a single, axiom-free rule of inference, in which a problem is solved by proving
1820-492: Is needed. Every OWL ontology must be identified by a URI (http://www.example.org/tea.owl, say). This example provides a sense of the syntax. To save space below, preambles and prefix definitions have been skipped. OWL classes correspond to description logic (DL) concepts , OWL properties to DL roles , while individuals are called the same way in both the OWL and the DL terminology. In
1890-402: Is normative. OWL2 specifies an XML serialization that closely models the structure of an OWL2 ontology. The Manchester Syntax is a compact, human readable syntax with a style close to frame languages. Variations are available for OWL and OWL2. Not all OWL and OWL2 ontologies can be expressed in this syntax. Consider an ontology for tea based on a Tea class. First, an ontology identifier
1960-519: Is not widely used. OWL DL is designed to provide the maximum expressiveness possible while retaining computational completeness (either φ or ¬φ holds), decidability (there is an effective procedure to determine whether φ is derivable or not), and the availability of practical reasoning algorithms. OWL DL includes all OWL language constructs, but they can be used only under certain restrictions (for example, number restrictions may not be placed upon properties which are declared to be transitive; and while
2030-400: Is reportedly the "most widely used learner" at Google, due in part to its scalability. Neural networks are also used as classifiers. An artificial neural network is based on a collection of nodes also known as artificial neurons , which loosely model the neurons in a biological brain. It is trained to recognise patterns; once trained, it can recognise those patterns in fresh data. There
2100-404: Is the process of proving a new statement ( conclusion ) from other statements that are given and assumed to be true (the premises ). Proofs can be structured as proof trees , in which nodes are labelled by sentences, and children nodes are connected to parent nodes by inference rules . Given a problem and a set of premises, problem-solving reduces to searching for a proof tree whose root node
2170-432: Is undecidable, so no reasoning software is able to perform complete reasoning for it. In OWL 2, there are three sublanguages of the language: The OWL family of languages supports a variety of syntaxes. It is useful to distinguish high level syntaxes aimed at specification from exchange syntaxes more suitable for general use. These are close to the ontology structure of languages in the OWL family. High level syntax
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2240-440: Is used for game-playing programs, such as chess or Go. It searches through a tree of possible moves and counter-moves, looking for a winning position. Local search uses mathematical optimization to find a solution to a problem. It begins with some form of guess and refines it incrementally. Gradient descent is a type of local search that optimizes a set of numerical parameters by incrementally adjusting them to minimize
2310-455: Is used for reasoning and knowledge representation . Formal logic comes in two main forms: propositional logic (which operates on statements that are true or false and uses logical connectives such as "and", "or", "not" and "implies") and predicate logic (which also operates on objects, predicates and relations and uses quantifiers such as " Every X is a Y " and "There are some X s that are Y s"). Deductive reasoning in logic
2380-436: Is used in AI programs that make decisions that involve other agents. Machine learning is the study of programs that can improve their performance on a given task automatically. It has been a part of AI from the beginning. There are several kinds of machine learning. Unsupervised learning analyzes a stream of data and finds patterns and makes predictions without any other guidance. Supervised learning requires labeling
2450-489: Is used to specify the OWL ontology structure and semantics. The OWL abstract syntax presents an ontology as a sequence of annotations , axioms and facts . Annotations carry machine and human oriented meta-data. Information about the classes, properties and individuals that compose the ontology is contained in axioms and facts only. Each class, property and individual is either anonymous or identified by an URI reference . Facts state data either about an individual or about
2520-905: Is when the knowledge gained from one problem is applied to a new problem. Deep learning is a type of machine learning that runs inputs through biologically inspired artificial neural networks for all of these types of learning. Computational learning theory can assess learners by computational complexity , by sample complexity (how much data is required), or by other notions of optimization . Natural language processing (NLP) allows programs to read, write and communicate in human languages such as English . Specific problems include speech recognition , speech synthesis , machine translation , information extraction , information retrieval and question answering . Early work, based on Noam Chomsky 's generative grammar and semantic networks , had difficulty with word-sense disambiguation unless restricted to small domains called " micro-worlds " (due to
2590-839: The World Wide Web Consortium 's (W3C) standard for objects called the Resource Description Framework (RDF). OWL and RDF have attracted significant academic, medical and commercial interest. In October 2007, a new W3C working group was started to extend OWL with several new features as proposed in the OWL 1.1 member submission. W3C announced the new version of OWL on 27 October 2009. This new version, called OWL 2, soon found its way into semantic editors such as Protégé and semantic reasoners such as Pellet, RacerPro, FaCT++ and HermiT. The OWL family contains many species, serializations, syntaxes and specifications with similar names. OWL and OWL2 are used to refer to
2660-520: The bar exam , SAT test, GRE test, and many other real-world applications. Machine perception is the ability to use input from sensors (such as cameras, microphones, wireless signals, active lidar , sonar, radar, and tactile sensors ) to deduce aspects of the world. Computer vision is the ability to analyze visual input. The field includes speech recognition , image classification , facial recognition , object recognition , object tracking , and robotic perception . Affective computing
2730-416: The transformer architecture , and by the early 2020s hundreds of billions of dollars were being invested in AI (known as the " AI boom "). The widespread use of AI in the 21st century exposed several unintended consequences and harms in the present and raised concerns about its risks and long-term effects in the future, prompting discussions about regulatory policies to ensure the safety and benefits of
2800-436: The " utility ") that measures how much the agent prefers it. For each possible action, it can calculate the " expected utility ": the utility of all possible outcomes of the action, weighted by the probability that the outcome will occur. It can then choose the action with the maximum expected utility. In classical planning , the agent knows exactly what the effect of any action will be. In most real-world problems, however,
2870-402: The "HasTypeABBlood" class. If it is stated that the individual Harriet is related via "hasMother" to the individual Sue, and that Harriet is a member of the "HasTypeOBlood" class, then it can be inferred that Sue is not a member of "HasTypeABBlood". This is, however, only true if the concepts of "Parent" and "Mother" only mean biological parent or mother and not social parent or mother. To choose
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2940-419: The 1970s. A 2006 survey of ontologies available on the web collected 688 OWL ontologies. Of these, 199 were OWL Lite, 149 were OWL DL and 337 OWL Full (by syntax). They found that 19 ontologies had in excess of 2,000 classes, and that 6 had more than 10,000. The same survey collected 587 RDFS vocabularies. An ontology is an explicit specification of a conceptualization. The data described by an ontology in
3010-456: The 2004 and 2009 specifications, respectively. Full species names will be used, including specification version (for example, OWL2 EL). When referring more generally, OWL Family will be used. There is a long history of ontological development in philosophy and computer science. Since the 1990s, a number of research efforts have explored how the idea of knowledge representation (KR) from artificial intelligence (AI) could be made useful on
3080-517: 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 structured sources of data such as corporate databases. The OWL languages are characterized by formal semantics . They are built upon
3150-444: The OWL family is interpreted as a set of "individuals" and a set of "property assertions" which relate these individuals to each other. An ontology consists of a set of axioms which place constraints on sets of individuals (called "classes") and the types of relationships permitted between them. These axioms provide semantics by allowing systems to infer additional information based on the data explicitly provided. A full introduction to
3220-460: The RDFS meaning, and OWL Full is a semantic extension of RDF. [The closed] world assumption implies that everything we don't know is false , while the open world assumption states that everything we don't know is undefined . The languages in the OWL family use the open world assumption . Under the open world assumption, if a statement cannot be proven to be true with current knowledge, we cannot draw
3290-720: The Semantic Web Activity in September 2007. In April 2008, this group decided to call this new language OWL2, indicating a substantial revision. OWL 2 became a W3C recommendation in October 2009. OWL 2 introduces profiles to improve scalability in typical applications. Why not be inconsistent in at least one aspect of a language which is all about consistency? OWL was chosen as an easily pronounced acronym that would yield good logos, suggest wisdom, and honor William A. Martin 's One World Language knowledge representation project from
3360-643: The World Wide Web. These included languages based on HTML (called SHOE ), based on XML (called XOL, later OIL ), and various frame-based KR languages and knowledge acquisition approaches. In 2000 in the United States, DARPA started development of DAML led by James Hendler . In March 2001, the Joint EU/US Committee on Agent Markup Languages decided that DAML should be merged with OIL. The EU/US ad hoc Joint Working Group on Agent Markup Languages
3430-421: The agent can seek information to improve its preferences. Information value theory can be used to weigh the value of exploratory or experimental actions. The space of possible future actions and situations is typically intractably large, so the agents must take actions and evaluate situations while being uncertain of what the outcome will be. A Markov decision process has a transition model that describes
3500-510: The agent may not be certain about the situation they are in (it is "unknown" or "unobservable") and it may not know for certain what will happen after each possible action (it is not "deterministic"). It must choose an action by making a probabilistic guess and then reassess the situation to see if the action worked. In some problems, the agent's preferences may be uncertain, especially if there are other agents or humans involved. These can be learned (e.g., with inverse reinforcement learning ), or
3570-529: The agent to operate with incomplete or uncertain information. AI researchers have devised a number of tools to solve these problems using methods from probability theory and economics. Precise mathematical tools have been developed that analyze how an agent can make choices and plan, using decision theory , decision analysis , and information value theory . These tools include models such as Markov decision processes , dynamic decision networks , game theory and mechanism design . Bayesian networks are
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#17328835768983640-576: The beginning, IS-A was quite simple. Today, however, there are almost as many meanings for this inheritance link as there are knowledge-representation systems. Early attempts to build large ontologies were plagued by a lack of clear definitions. Members of the OWL family have model theoretic formal semantics, and so have strong logical foundations. Description logics are a family of logics that are decidable fragments of first-order logic with attractive and well-understood computational properties. OWL DL and OWL Lite semantics are based on DLs. They combine
3710-648: The common sense knowledge problem ). Margaret Masterman believed that it was meaning and not grammar that was the key to understanding languages, and that thesauri and not dictionaries should be the basis of computational language structure. Modern deep learning techniques for NLP include word embedding (representing words, typically as vectors encoding their meaning), transformers (a deep learning architecture using an attention mechanism), and others. In 2019, generative pre-trained transformer (or "GPT") language models began to generate coherent text, and by 2023, these models were able to get human-level scores on
3780-427: The conclusion that the statement is false. A relational database consists of sets of tuples with the same attributes . SQL is a query and management language for relational databases. Prolog is a logical programming language. Both use the closed world assumption . The following tools include public ontology browsers: Artificial intelligence Artificial intelligence ( AI ), in its broadest sense,
3850-493: The expressive power of the OWL is provided in the W3C's OWL Guide . OWL ontologies can import other ontologies, adding information from the imported ontology to the current ontology. An ontology describing families might include axioms stating that a "hasMother" property is only present between two individuals when "hasParent" is also present, and that individuals of class "HasTypeOBlood" are never related via "hasParent" to members of
3920-548: The expressiveness constraints placed on OWL Lite amount to little more than syntactic inconveniences: most of the constructs available in OWL DL can be built using complex combinations of OWL Lite features, and is equally expressive as the description logic S H I F ( D ) {\displaystyle {\mathcal {SHIF}}(\mathbf {D} )} . Development of OWL Lite tools has thus proven to be almost as difficult as development of tools for OWL DL, and OWL Lite
3990-440: The intelligence of existing computer agents. Moderate successes related to affective computing include textual sentiment analysis and, more recently, multimodal sentiment analysis , wherein AI classifies the affects displayed by a videotaped subject. A machine with artificial general intelligence should be able to solve a wide variety of problems with breadth and versatility similar to human intelligence . AI research uses
4060-419: The knowledge in an ontology) exist for these DLs. OWL Full is intended to be compatible with RDF Schema (RDFS), and to be capable of augmenting the meanings of existing Resource Description Framework (RDF) vocabulary. A model theory describes the formal semantics for RDF. This interpretation provides the meaning of RDF and RDFS vocabulary. So, the meaning of OWL Full ontologies are defined by extension of
4130-537: The late 1980s and 1990s, methods were developed for dealing with uncertain or incomplete information, employing concepts from probability and economics . Many of these algorithms are insufficient for solving large reasoning problems because they experience a "combinatorial explosion": They become exponentially slower as the problems grow. Even humans rarely use the step-by-step deduction that early AI research could model. They solve most of their problems using fast, intuitive judgments. Accurate and efficient reasoning
4200-480: The letters logic above. OWL Full is based on a different semantics from OWL Lite or OWL DL, and was designed to preserve some compatibility with RDF Schema. For example, in OWL Full a class can be treated simultaneously as a collection of individuals and as an individual in its own right; this is not permitted in OWL DL. OWL Full allows an ontology to augment the meaning of the pre-defined (RDF or OWL) vocabulary. OWL Full
4270-413: The link to point directly to the intended article. Retrieved from " https://en.wikipedia.org/w/index.php?title=DQL&oldid=813426749 " Categories : Disambiguation pages Query languages Hidden categories: Short description is different from Wikidata All article disambiguation pages All disambiguation pages DAML%2BOIL The Web Ontology Language ( OWL )
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#17328835768984340-457: The most difficult problems in knowledge representation are the breadth of commonsense knowledge (the set of atomic facts that the average person knows is enormous); and the sub-symbolic form of most commonsense knowledge (much of what people know is not represented as "facts" or "statements" that they could express verbally). There is also the difficulty of knowledge acquisition , the problem of obtaining knowledge for AI applications. An "agent"
4410-428: The name abstract syntax may be somewhat misleading. This syntax closely follows the structure of an OWL2 ontology. It is used by OWL2 to specify semantics, mappings to exchange syntaxes and profiles. Syntactic mappings into RDF are specified for languages in the OWL family. Several RDF serialization formats have been devised. Each leads to a syntax for languages in the OWL family through this mapping. RDF/XML
4480-405: The other hand. Classifiers are functions that use pattern matching to determine the closest match. They can be fine-tuned based on chosen examples using supervised learning . Each pattern (also called an " observation ") is labeled with a certain predefined class. All the observations combined with their class labels are known as a data set . When a new observation is received, that observation
4550-411: The probability that a particular action will change the state in a particular way and a reward function that supplies the utility of each state and the cost of each action. A policy associates a decision with each possible state. The policy could be calculated (e.g., by iteration ), be heuristic , or it can be learned. Game theory describes the rational behavior of multiple interacting agents and
4620-471: The technology . The general problem of simulating (or creating) intelligence has been broken into subproblems. These consist of particular traits or capabilities that researchers expect an intelligent system to display. The traits described below have received the most attention and cover the scope of AI research. Early researchers developed algorithms that imitated step-by-step reasoning that humans use when they solve puzzles or make logical deductions . By
4690-451: The training data with the expected answers, and comes in two main varieties: classification (where the program must learn to predict what category the input belongs in) and regression (where the program must deduce a numeric function based on numeric input). In reinforcement learning , the agent is rewarded for good responses and punished for bad ones. The agent learns to choose responses that are classified as "good". Transfer learning
4760-420: The use of particular tools. The traditional goals of AI research include reasoning , knowledge representation , planning , learning , natural language processing , perception, and support for robotics . General intelligence —the ability to complete any task performable by a human on an at least equal level—is among the field's long-term goals. To reach these goals, AI researchers have adapted and integrated
4830-616: The working group was disbanded on May 31, 2004. In 2005, at the OWL Experiences And Directions Workshop a consensus formed that recent advances in description logic would allow a more expressive revision to satisfy user requirements more comprehensively whilst retaining good computational properties. In December 2006, the OWL1.1 Member Submission was made to the W3C. The W3C chartered the OWL Working Group as part of
4900-614: Was convened to develop DAML+OIL as a web ontology language. This group was jointly funded by the DARPA (under the DAML program) and the European Union's Information Society Technologies (IST) funding project. DAML+OIL was intended to be a thin layer above RDFS , with formal semantics based on a description logic (DL). DAML+OIL is a particularly major influence on OWL; OWL's design was specifically based on DAML+OIL. The Semantic Web provides
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