Case-based Reasoning
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Case-based Reasoning
In artificial intelligence and philosophy, case-based reasoning (CBR), broadly construed, is the process of solving new problems based on the solutions of similar past problems. In everyday life, an auto mechanic who fixes an engine by recalling another car that exhibited similar symptoms is using case-based reasoning. A lawyer who advocates a particular outcome in a trial based on legal precedents or a judge who creates case law is using case-based reasoning. So, too, an engineer copying working elements of nature (practicing biomimicry), is treating nature as a database of solutions to problems. Case-based reasoning is a prominent type of analogy solution making. It has been argued that case-based reasoning is not only a powerful method for computer reasoning, but also a pervasive behavior in everyday human problem solving; or, more radically, that all reasoning is based on past cases personally experienced. This view is related to prototype theory, which is most deepl ...
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Artificial Intelligence
Artificial intelligence (AI) is intelligence—perceiving, synthesizing, and inferring information—demonstrated by machines, as opposed to intelligence displayed by animals and humans. Example tasks in which this is done include speech recognition, computer vision, translation between (natural) languages, as well as other mappings of inputs. The ''Oxford English Dictionary'' of Oxford University Press defines artificial intelligence as: the theory and development of computer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages. AI applications include advanced web search engines (e.g., Google), recommendation systems (used by YouTube, Amazon and Netflix), understanding human speech (such as Siri and Alexa), self-driving cars (e.g., Tesla), automated decision-making and competing at the highest level in strategic game systems (such as chess and Go). ...
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CBR Steps
CBR may refer to: Business and organizations * Central Bank of Russia * Central Board of Revenue, the Pakistan department for revenue collection * CBR, formerly the New York Stock Exchange symbol for Ciber * Center for Bio-Ethical Reform, an anti-abortion non-profit organization * Centre for Blood Research at the University of British Columbia * CBR (''Cimenteries et Briqueteries Réunies''), now HeidelbergCement * Championship Bull Riding, a rodeo organization in Texas, USA Comics * .cbr, a file extension for comic book archive files * ''Comic Book Resources'', a news and discussion website Radio * CBR (AM), a radio station in Calgary, Alberta, Canada * CBR-FM, a radio station in Calgary, Alberta, Canada * CBR, former call sign of radio station CBU in Vancouver, British Columbia, Canada Science, medicine, and engineering * California bearing ratio, a strength measurement of material under a paved area * Cannabinoid receptor, a type of cell membrane receptor * Case-based reas ...
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Duck Test
The duck test is a form of abductive reasoning, usually expressed as "If it looks like a duck, swims like a duck, and quacks like a duck, then it probably is a duck." The test implies that a person can identify an unknown subject by observing that subject's habitual characteristics. It is sometimes used to counter abstruse arguments that something is not what it appears to be. History The French automaton maker Jacques de Vaucanson created a mechanical duck in 1738. The mechanical duck would quack, move its head to eat grain which it would appear to digest, and after a short time would excrete a mixture that looked and smelled like duck droppings. The irony is that while the phrase is often cited as proof of abductive reasoning, it is not proof, as the mechanical duck is still not a living duck. In popular culture Douglas Adams parodied this test in his book ''Dirk Gently's Holistic Detective Agency'': Monty Python also referenced the test in the Witch Logic scene i ...
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Abductive Reasoning
Abductive reasoning (also called abduction,For example: abductive inference, or retroduction) is a form of logical inference formulated and advanced by American philosopher Charles Sanders Peirce beginning in the last third of the 19th century. It starts with an observation or set of observations and then seeks the simplest and most likely conclusion from the observations. This process, unlike deductive reasoning, yields a plausible conclusion but does not positively verify it. Abductive conclusions are thus qualified as having a remnant of uncertainty or doubt, which is expressed in retreat terms such as "best available" or "most likely". One can understand abductive reasoning as inference to the best explanation, although not all usages of the terms ''abduction'' and ''inference to the best explanation'' are exactly equivalent. In the 1990s, as computing power grew, the fields of law, computer science, and artificial intelligence researchFor examples, seeAbductive Inference i ...
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Statistical Inference
Statistical inference is the process of using data analysis to infer properties of an underlying probability distribution, distribution of probability.Upton, G., Cook, I. (2008) ''Oxford Dictionary of Statistics'', OUP. . Inferential statistical analysis infers properties of a Statistical population, population, for example by testing hypotheses and deriving estimates. It is assumed that the observed data set is Sampling (statistics), sampled from a larger population. Inferential statistics can be contrasted with descriptive statistics. Descriptive statistics is solely concerned with properties of the observed data, and it does not rest on the assumption that the data come from a larger population. In machine learning, the term ''inference'' is sometimes used instead to mean "make a prediction, by evaluating an already trained model"; in this context inferring properties of the model is referred to as ''training'' or ''learning'' (rather than ''inference''), and using a model for ...
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Janet Kolodner
Janet Lynne Kolodner is an American cognitive scientist and learning scientist and Regents' Professor Emerita in the School of Interactive Computing, College of Computing at the Georgia Institute of Technology. She was Founding Editor in Chief of ''The Journal of the Learning Sciences'' and served in that role for 19 years. She was Founding Executive Officer of the International Society of the Learning Sciences (ISLS). From August, 2010 through July, 2014, she was a program officer at the National Science Foundation and headed up the Cyberlearning and Future Learning Technologies program (originally called Cyberlearning: Transforming Education). Since finishing at NSF, she is working toward a set of projects that will integrate learning technologies coherently to support disciplinary and everyday learning, support project-based pedagogy that works, and connect to the best in curriculum for active learning. As of July, 2020, she is a Professor of the Practice at the Lynch Schoo ...
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Yale University
Yale University is a private research university in New Haven, Connecticut. Established in 1701 as the Collegiate School, it is the third-oldest institution of higher education in the United States and among the most prestigious in the world. It is a member of the Ivy League. Chartered by the Connecticut Colony, the Collegiate School was established in 1701 by clergy to educate Congregational ministers before moving to New Haven in 1716. Originally restricted to theology and sacred languages, the curriculum began to incorporate humanities and sciences by the time of the American Revolution. In the 19th century, the college expanded into graduate and professional instruction, awarding the first PhD in the United States in 1861 and organizing as a university in 1887. Yale's faculty and student populations grew after 1890 with rapid expansion of the physical campus and scientific research. Yale is organized into fourteen constituent schools: the original undergraduate col ...
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Roger Schank
Roger Carl Schank (born 1946) is an American artificial intelligence theorist, cognitive psychologist, learning scientist, educational reformer, and entrepreneur. Beginning in the late 1960s, he pioneered conceptual dependency theory (within the context of natural language understanding) and case-based reasoning, both of which challenged cognitivist views of memory and reasoning. In 1989, Schank was granted $30 million in a 10-year commitment to his research and development by Andersen Consulting, through which he founded the Institute for the Learning Sciences (ILS) at Northwestern University in Chicago. Academic career For his undergraduate degree, Schank studied mathematics at Carnegie Mellon University in Pittsburgh PA, and later was awarded a PhD in linguistics at the University of Texas in Austin and went on to work in faculty positions at Stanford University and then at Yale University. In 1974, he became professor of computer science and psychology at Yale University. I ...
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Inductive Reasoning
Inductive reasoning is a method of reasoning in which a general principle is derived from a body of observations. It consists of making broad generalizations based on specific observations. Inductive reasoning is distinct from ''deductive'' reasoning. If the premises are correct, the conclusion of a deductive argument is ''certain''; in contrast, the truth of the conclusion of an inductive argument is '' probable'', based upon the evidence given. Types The types of inductive reasoning include generalization, prediction, statistical syllogism, argument from analogy, and causal inference. Inductive generalization A generalization (more accurately, an ''inductive generalization'') proceeds from a premise about a sample to a conclusion about the population. The observation obtained from this sample is projected onto the broader population. : The proportion Q of the sample has attribute A. : Therefore, the proportion Q of the population has attribute A. For example, say there ...
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Anecdotal Evidence
Anecdotal evidence is evidence based only on personal observation, collected in a casual or non-systematic manner. The term is sometimes used in a legal context to describe certain kinds of testimony which are uncorroborated by objective, independent evidence such as notarized documentation, photographs, audio-visual recordings, etc. When used in advertising or promotion of a product, service, or idea, anecdotal reports are often called a testimonial, which are highly regulated in some jurisdictions. When compared to other types of evidence, anecdotal evidence is generally regarded as limited in value due to a number of potential weaknesses, but may be considered within the scope of scientific method as some anecdotal evidence can be both empirical and verifiable, e.g. in the use of case studies in medicine. Other anecdotal evidence, however, does not qualify as scientific evidence, because its nature prevents it from being investigated by the scientific method. Where only one or ...
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Springer-Verlag
Springer Science+Business Media, commonly known as Springer, is a German multinational publishing company of books, e-books and peer-reviewed journals in science, humanities, technical and medical (STM) publishing. Originally founded in 1842 in Berlin, it expanded internationally in the 1960s, and through mergers in the 1990s and a sale to venture capitalists it fused with Wolters Kluwer and eventually became part of Springer Nature in 2015. Springer has major offices in Berlin, Heidelberg, Dordrecht, and New York City. History Julius Springer founded Springer-Verlag in Berlin in 1842 and his son Ferdinand Springer grew it from a small firm of 4 employees into Germany's then second largest academic publisher with 65 staff in 1872.Chronology
". Springer Science+Business Media.
In 1964, Springer expanded its business internationally, o ...
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Machine Learning
Machine learning (ML) is a field of inquiry devoted to understanding and building methods that 'learn', that is, methods that leverage data to improve performance on some set of tasks. It is seen as a part of artificial intelligence. Machine learning algorithms build a model based on sample data, known as training data, in order to make predictions or decisions without being explicitly programmed to do so. Machine learning algorithms are used in a wide variety of applications, such as in medicine, email filtering, speech recognition, agriculture, and computer vision, where it is difficult or unfeasible to develop conventional algorithms to perform the needed tasks.Hu, J.; Niu, H.; Carrasco, J.; Lennox, B.; Arvin, F.,Voronoi-Based Multi-Robot Autonomous Exploration in Unknown Environments via Deep Reinforcement Learning IEEE Transactions on Vehicular Technology, 2020. A subset of machine learning is closely related to computational statistics, which focuses on making predicti ...
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