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61 first-order predicate calculus
Англо-русский толковый словарь терминов и сокращений по ВТ, Интернету и программированию. > first-order predicate calculus
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62 lower predicate calculus
= LPC II; = l.p.c.Англо-русский толковый словарь терминов и сокращений по ВТ, Интернету и программированию. > lower predicate calculus
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63 логика предикатове
Большой англо-русский и русско-английский словарь > логика предикатове
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64 language
язык || языковой- action description language
- actual machine language
- agent programming language
- AI language
- Algol-like language
- algorithmical language
- algorithmic language
- application-oriented language
- applicative language
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- data definition language
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- data language
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- database language
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- original language
- page description language
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- predicate logic-based language
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- privacy language
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- pseudo language
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- user language
- user-oriented language
- very-high-level languageEnglish-Russian dictionary of computer science and programming > language
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65 language
1) язык || языковой2) машинный язык; набор символов ( машины)•- application-oriented language
- applicative language
- APT programming language
- APT-based language
- artificial language
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- assembly language
- block diagram language
- calculus language
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- predicate language
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- problem-oriented language
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- Siman simulation language
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- source language
- special interface programming language
- specification language
- state language
- structured query language
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- task description language
- task level language
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- uncontrolled language
- very high level languageEnglish-Russian dictionary of mechanical engineering and automation > language
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66 set-theoretic
теоретико-множественный set-theoretic predicate logic ≈ теоретико-множественная логика предикатов - set-theoretic geometry - set-theoretic interpretation - set-theoretic intersection - set-theoretic jog - set-theoretic operation - set-theoretic projection - set-theoretic statement - set-theoretic topology - set-theoretic unionБольшой англо-русский и русско-английский словарь > set-theoretic
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67 PL
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68 calculus
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69 Artificial Intelligence
In my opinion, none of [these programs] does even remote justice to the complexity of human mental processes. Unlike men, "artificially intelligent" programs tend to be single minded, undistractable, and unemotional. (Neisser, 1967, p. 9)Future progress in [artificial intelligence] will depend on the development of both practical and theoretical knowledge.... As regards theoretical knowledge, some have sought a unified theory of artificial intelligence. My view is that artificial intelligence is (or soon will be) an engineering discipline since its primary goal is to build things. (Nilsson, 1971, pp. vii-viii)Most workers in AI [artificial intelligence] research and in related fields confess to a pronounced feeling of disappointment in what has been achieved in the last 25 years. Workers entered the field around 1950, and even around 1960, with high hopes that are very far from being realized in 1972. In no part of the field have the discoveries made so far produced the major impact that was then promised.... In the meantime, claims and predictions regarding the potential results of AI research had been publicized which went even farther than the expectations of the majority of workers in the field, whose embarrassments have been added to by the lamentable failure of such inflated predictions....When able and respected scientists write in letters to the present author that AI, the major goal of computing science, represents "another step in the general process of evolution"; that possibilities in the 1980s include an all-purpose intelligence on a human-scale knowledge base; that awe-inspiring possibilities suggest themselves based on machine intelligence exceeding human intelligence by the year 2000 [one has the right to be skeptical]. (Lighthill, 1972, p. 17)4) Just as Astronomy Succeeded Astrology, the Discovery of Intellectual Processes in Machines Should Lead to a Science, EventuallyJust as astronomy succeeded astrology, following Kepler's discovery of planetary regularities, the discoveries of these many principles in empirical explorations on intellectual processes in machines should lead to a science, eventually. (Minsky & Papert, 1973, p. 11)5) Problems in Machine Intelligence Arise Because Things Obvious to Any Person Are Not Represented in the ProgramMany problems arise in experiments on machine intelligence because things obvious to any person are not represented in any program. One can pull with a string, but one cannot push with one.... Simple facts like these caused serious problems when Charniak attempted to extend Bobrow's "Student" program to more realistic applications, and they have not been faced up to until now. (Minsky & Papert, 1973, p. 77)What do we mean by [a symbolic] "description"? We do not mean to suggest that our descriptions must be made of strings of ordinary language words (although they might be). The simplest kind of description is a structure in which some features of a situation are represented by single ("primitive") symbols, and relations between those features are represented by other symbols-or by other features of the way the description is put together. (Minsky & Papert, 1973, p. 11)[AI is] the use of computer programs and programming techniques to cast light on the principles of intelligence in general and human thought in particular. (Boden, 1977, p. 5)The word you look for and hardly ever see in the early AI literature is the word knowledge. They didn't believe you have to know anything, you could always rework it all.... In fact 1967 is the turning point in my mind when there was enough feeling that the old ideas of general principles had to go.... I came up with an argument for what I called the primacy of expertise, and at the time I called the other guys the generalists. (Moses, quoted in McCorduck, 1979, pp. 228-229)9) Artificial Intelligence Is Psychology in a Particularly Pure and Abstract FormThe basic idea of cognitive science is that intelligent beings are semantic engines-in other words, automatic formal systems with interpretations under which they consistently make sense. We can now see why this includes psychology and artificial intelligence on a more or less equal footing: people and intelligent computers (if and when there are any) turn out to be merely different manifestations of the same underlying phenomenon. Moreover, with universal hardware, any semantic engine can in principle be formally imitated by a computer if only the right program can be found. And that will guarantee semantic imitation as well, since (given the appropriate formal behavior) the semantics is "taking care of itself" anyway. Thus we also see why, from this perspective, artificial intelligence can be regarded as psychology in a particularly pure and abstract form. The same fundamental structures are under investigation, but in AI, all the relevant parameters are under direct experimental control (in the programming), without any messy physiology or ethics to get in the way. (Haugeland, 1981b, p. 31)There are many different kinds of reasoning one might imagine:Formal reasoning involves the syntactic manipulation of data structures to deduce new ones following prespecified rules of inference. Mathematical logic is the archetypical formal representation. Procedural reasoning uses simulation to answer questions and solve problems. When we use a program to answer What is the sum of 3 and 4? it uses, or "runs," a procedural model of arithmetic. Reasoning by analogy seems to be a very natural mode of thought for humans but, so far, difficult to accomplish in AI programs. The idea is that when you ask the question Can robins fly? the system might reason that "robins are like sparrows, and I know that sparrows can fly, so robins probably can fly."Generalization and abstraction are also natural reasoning process for humans that are difficult to pin down well enough to implement in a program. If one knows that Robins have wings, that Sparrows have wings, and that Blue jays have wings, eventually one will believe that All birds have wings. This capability may be at the core of most human learning, but it has not yet become a useful technique in AI.... Meta- level reasoning is demonstrated by the way one answers the question What is Paul Newman's telephone number? You might reason that "if I knew Paul Newman's number, I would know that I knew it, because it is a notable fact." This involves using "knowledge about what you know," in particular, about the extent of your knowledge and about the importance of certain facts. Recent research in psychology and AI indicates that meta-level reasoning may play a central role in human cognitive processing. (Barr & Feigenbaum, 1981, pp. 146-147)Suffice it to say that programs already exist that can do things-or, at the very least, appear to be beginning to do things-which ill-informed critics have asserted a priori to be impossible. Examples include: perceiving in a holistic as opposed to an atomistic way; using language creatively; translating sensibly from one language to another by way of a language-neutral semantic representation; planning acts in a broad and sketchy fashion, the details being decided only in execution; distinguishing between different species of emotional reaction according to the psychological context of the subject. (Boden, 1981, p. 33)Can the synthesis of Man and Machine ever be stable, or will the purely organic component become such a hindrance that it has to be discarded? If this eventually happens-and I have... good reasons for thinking that it must-we have nothing to regret and certainly nothing to fear. (Clarke, 1984, p. 243)The thesis of GOFAI... is not that the processes underlying intelligence can be described symbolically... but that they are symbolic. (Haugeland, 1985, p. 113)14) Artificial Intelligence Provides a Useful Approach to Psychological and Psychiatric Theory FormationIt is all very well formulating psychological and psychiatric theories verbally but, when using natural language (even technical jargon), it is difficult to recognise when a theory is complete; oversights are all too easily made, gaps too readily left. This is a point which is generally recognised to be true and it is for precisely this reason that the behavioural sciences attempt to follow the natural sciences in using "classical" mathematics as a more rigorous descriptive language. However, it is an unfortunate fact that, with a few notable exceptions, there has been a marked lack of success in this application. It is my belief that a different approach-a different mathematics-is needed, and that AI provides just this approach. (Hand, quoted in Hand, 1985, pp. 6-7)We might distinguish among four kinds of AI.Research of this kind involves building and programming computers to perform tasks which, to paraphrase Marvin Minsky, would require intelligence if they were done by us. Researchers in nonpsychological AI make no claims whatsoever about the psychological realism of their programs or the devices they build, that is, about whether or not computers perform tasks as humans do.Research here is guided by the view that the computer is a useful tool in the study of mind. In particular, we can write computer programs or build devices that simulate alleged psychological processes in humans and then test our predictions about how the alleged processes work. We can weave these programs and devices together with other programs and devices that simulate different alleged mental processes and thereby test the degree to which the AI system as a whole simulates human mentality. According to weak psychological AI, working with computer models is a way of refining and testing hypotheses about processes that are allegedly realized in human minds.... According to this view, our minds are computers and therefore can be duplicated by other computers. Sherry Turkle writes that the "real ambition is of mythic proportions, making a general purpose intelligence, a mind." (Turkle, 1984, p. 240) The authors of a major text announce that "the ultimate goal of AI research is to build a person or, more humbly, an animal." (Charniak & McDermott, 1985, p. 7)Research in this field, like strong psychological AI, takes seriously the functionalist view that mentality can be realized in many different types of physical devices. Suprapsychological AI, however, accuses strong psychological AI of being chauvinisticof being only interested in human intelligence! Suprapsychological AI claims to be interested in all the conceivable ways intelligence can be realized. (Flanagan, 1991, pp. 241-242)16) Determination of Relevance of Rules in Particular ContextsEven if the [rules] were stored in a context-free form the computer still couldn't use them. To do that the computer requires rules enabling it to draw on just those [ rules] which are relevant in each particular context. Determination of relevance will have to be based on further facts and rules, but the question will again arise as to which facts and rules are relevant for making each particular determination. One could always invoke further facts and rules to answer this question, but of course these must be only the relevant ones. And so it goes. It seems that AI workers will never be able to get started here unless they can settle the problem of relevance beforehand by cataloguing types of context and listing just those facts which are relevant in each. (Dreyfus & Dreyfus, 1986, p. 80)Perhaps the single most important idea to artificial intelligence is that there is no fundamental difference between form and content, that meaning can be captured in a set of symbols such as a semantic net. (G. Johnson, 1986, p. 250)Artificial intelligence is based on the assumption that the mind can be described as some kind of formal system manipulating symbols that stand for things in the world. Thus it doesn't matter what the brain is made of, or what it uses for tokens in the great game of thinking. Using an equivalent set of tokens and rules, we can do thinking with a digital computer, just as we can play chess using cups, salt and pepper shakers, knives, forks, and spoons. Using the right software, one system (the mind) can be mapped into the other (the computer). (G. Johnson, 1986, p. 250)19) A Statement of the Primary and Secondary Purposes of Artificial IntelligenceThe primary goal of Artificial Intelligence is to make machines smarter.The secondary goals of Artificial Intelligence are to understand what intelligence is (the Nobel laureate purpose) and to make machines more useful (the entrepreneurial purpose). (Winston, 1987, p. 1)The theoretical ideas of older branches of engineering are captured in the language of mathematics. We contend that mathematical logic provides the basis for theory in AI. Although many computer scientists already count logic as fundamental to computer science in general, we put forward an even stronger form of the logic-is-important argument....AI deals mainly with the problem of representing and using declarative (as opposed to procedural) knowledge. Declarative knowledge is the kind that is expressed as sentences, and AI needs a language in which to state these sentences. Because the languages in which this knowledge usually is originally captured (natural languages such as English) are not suitable for computer representations, some other language with the appropriate properties must be used. It turns out, we think, that the appropriate properties include at least those that have been uppermost in the minds of logicians in their development of logical languages such as the predicate calculus. Thus, we think that any language for expressing knowledge in AI systems must be at least as expressive as the first-order predicate calculus. (Genesereth & Nilsson, 1987, p. viii)21) Perceptual Structures Can Be Represented as Lists of Elementary PropositionsIn artificial intelligence studies, perceptual structures are represented as assemblages of description lists, the elementary components of which are propositions asserting that certain relations hold among elements. (Chase & Simon, 1988, p. 490)Artificial intelligence (AI) is sometimes defined as the study of how to build and/or program computers to enable them to do the sorts of things that minds can do. Some of these things are commonly regarded as requiring intelligence: offering a medical diagnosis and/or prescription, giving legal or scientific advice, proving theorems in logic or mathematics. Others are not, because they can be done by all normal adults irrespective of educational background (and sometimes by non-human animals too), and typically involve no conscious control: seeing things in sunlight and shadows, finding a path through cluttered terrain, fitting pegs into holes, speaking one's own native tongue, and using one's common sense. Because it covers AI research dealing with both these classes of mental capacity, this definition is preferable to one describing AI as making computers do "things that would require intelligence if done by people." However, it presupposes that computers could do what minds can do, that they might really diagnose, advise, infer, and understand. One could avoid this problematic assumption (and also side-step questions about whether computers do things in the same way as we do) by defining AI instead as "the development of computers whose observable performance has features which in humans we would attribute to mental processes." This bland characterization would be acceptable to some AI workers, especially amongst those focusing on the production of technological tools for commercial purposes. But many others would favour a more controversial definition, seeing AI as the science of intelligence in general-or, more accurately, as the intellectual core of cognitive science. As such, its goal is to provide a systematic theory that can explain (and perhaps enable us to replicate) both the general categories of intentionality and the diverse psychological capacities grounded in them. (Boden, 1990b, pp. 1-2)Because the ability to store data somewhat corresponds to what we call memory in human beings, and because the ability to follow logical procedures somewhat corresponds to what we call reasoning in human beings, many members of the cult have concluded that what computers do somewhat corresponds to what we call thinking. It is no great difficulty to persuade the general public of that conclusion since computers process data very fast in small spaces well below the level of visibility; they do not look like other machines when they are at work. They seem to be running along as smoothly and silently as the brain does when it remembers and reasons and thinks. On the other hand, those who design and build computers know exactly how the machines are working down in the hidden depths of their semiconductors. Computers can be taken apart, scrutinized, and put back together. Their activities can be tracked, analyzed, measured, and thus clearly understood-which is far from possible with the brain. This gives rise to the tempting assumption on the part of the builders and designers that computers can tell us something about brains, indeed, that the computer can serve as a model of the mind, which then comes to be seen as some manner of information processing machine, and possibly not as good at the job as the machine. (Roszak, 1994, pp. xiv-xv)The inner workings of the human mind are far more intricate than the most complicated systems of modern technology. Researchers in the field of artificial intelligence have been attempting to develop programs that will enable computers to display intelligent behavior. Although this field has been an active one for more than thirty-five years and has had many notable successes, AI researchers still do not know how to create a program that matches human intelligence. No existing program can recall facts, solve problems, reason, learn, and process language with human facility. This lack of success has occurred not because computers are inferior to human brains but rather because we do not yet know in sufficient detail how intelligence is organized in the brain. (Anderson, 1995, p. 2)Historical dictionary of quotations in cognitive science > Artificial Intelligence
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70 monadic
компьют. одноместный monadic [monic] homomorphism ≈ инъективный гомоморфизм, мономорфизм monadic predicate calculus ≈ исчисление одноместных предикатов - monadic algebra - monadic continuity - monadic game - monadic ideal - monadic logic - monadic operation - monadic operator - monadic predicate Одноместно -
71 programming
1) вчт программирование•- applications programming
- automatic programming
- bare metal programming
- beam programming
- bottom-up programming
- business programming
- cascade programming
- compositional parallel programming
- compromise programming
- conceptual programming
- concurrent programming
- configuration programming
- declarative programming
- distributed logic programming
- dynamic programming
- egoless programming
- event-driven programming
- flow programming
- functional programming
- fuzzy programming
- generic programming
- genetic programming
- geometric programming
- goal-driven programming
- heuristic programming
- high-level language programming
- imperative programming
- inference programming
- integer programming
- interactive programming
- interpretive programming
- linear programming
- linear integer programming
- logic programming
- macro programming
- maintenance programming
- manual programming
- mathematical programming
- metalevel programming
- mixed integer programming
- modular programming
- modular parallel programming
- molecular programming
- multicriteria programming
- neurodynamic programming
- neurolinguistic programming
- nonlinear programming
- nonnumeric programming
- object-based programming
- object-language programming
- object-oriented programming
- off-line programming
- optimum programming
- parallel programming
- parallel programming in transputer medium
- parallel programming with coordination structures
- paranoid programming
- polynomial programming
- possibilistic programming
- predicate programming
- procedure-oriented programming
- production programming
- quadrature programming
- recursive quadratic programming
- stochastic programming
- structural programming
- structured programming
- switchboard programming
- symbolic programming
- symbolic-language programming
- system programming
- systems programming
- test programming
- top-down programming
- traditional programming
- typematic delay programming
- typematic rate programming
- visual programming -
72 function
1) функция2) функционировать; находиться в работоспособном состоянии3) выполнять функцию; играть роль4) (дополнительное) функциональное устройство, проф. функция ( в стандарте USB)5) вчт. отображение || отображать•- actuating transfer function
- additive function
- additive/multiplicative function
- admittance function
- advanced communication function
- affine Boolean function
- aggregate function
- algebraic function
- all-pass transfer function
- all-pole function
- all-zero function
- alternating function
- ambiguity function
- amplitude distribution function
- amplitude function
- AM-tive function additive/multiplicative function
- anode work function
- aperture phase function
- apodizing function
- application program function
- autocorrelation function
- automatic azimuth alignment function
- band-limited function
- base station control function
- basis function
- Bellman function
- bent function
- Bessel function of imaginary argument
- Bessel function
- beta function
- bijection function
- bijective function
- binary activation function
- binary sigmoid function
- binate function
- bipolar sigmoid function
- bi-state function
- bivariate distribution function
- Boolean function
- Bose-Einstein distribution function
- bounded function
- boxcar function
- Brillouin function
- built-in function
- Butterworth function
- carpet function
- carrier function
- cathode work function
- characteristic function
- circular function
- closed function
- closed-loop transfer function
- clutching function
- coherence function
- color matching functions
- comb function
- combination function
- combining function
- competitive function
- complementary error function
- complementary function
- composite function
- computable function
- concentrated likelihood function
- continuous function
- contrast transfer function
- control function
- convolution function
- correlation function
- cost function
- covariance generating function
- criterion function
- cross-correlation function - current potential function
- current transfer function
- curried function
- data communications function
- data-path function
- decision function
- degate function
- degating function
- delta function
- demand function
- density function
- descrambling function
- describing function
- difference transfer function
- differentiable function
- digamma function
- Dirac delta function
- Dirac function
- disconnect-reconnect function
- discriminant function
- distribution function
- driving-point function
- eikonal function
- electron wave function
- embedding function
- encryption function
- ergodic function
- error function
- excitation function
- explicit function
- exponential function
- extensional function
- external function
- failure density function
- feedback transfer function
- Fermi function
- Fermi-Dirac distribution function
- force function
- forward transfer function
- frequency function
- frequency-generating function
- frequency-response function
- friend function
- FS function
- full-speed function
- fuzzy function
- fuzzy objective function
- fuzzy utility function
- gage function
- Gaussian function
- Gaussian radial basis function
- generalized function
- generic function
- global implicit function
- global inverse function
- Green functions
- Green's function
- Hamilton function
- Hankel function
- hard limit activation function - hazard function
- head-related transfer function
- Heaviside step function
- Huber function
- hyperbolic function
- hyperbolic tangent activation function
- idempotent function
- image function
- impedance function
- implicit function
- injection function
- injective function
- inline function
- intensional function
- interference function
- interworking function
- inverse distribution function
- inverse function
- invertible mapping function
- inverting function
- kernel function
- Lagrange's function
- Langevin function
- latent function
- Legendre associated function of the first kind
- Legendre associated function of the second kind
- Legendre function of the first kind
- Legendre function of the second kind
- lexical function
- likelihood function
- line search function
- linear function
- linear logic function
- logic function
- logistic function
- logistic sigmoid function
- log-likelihood function
- log-linear function
- log-log function
- look-up function
- loss function
- low-speed function
- LS function
- luminosity function
- macro function
- main function
- maintenance entity function
- majorized function
- majorizing function
- mapping function
- Markov function
- mathematical function
- member function
- membership function
- memo function
- memoised function
- memoized function
- minorized function
- minorizing function
- modified Bessel function
- modular hash-function
- modulating function
- modulation transfer function
- moment-generating function
- monotonic function
- Morse function
- multi-input multi-output transfer function
- multi-valued function
- multivariate distribution function
- mutual coherence function
- natural trigonometric function
- never-decreasing function
- never-increasing function
- non-decreasing function
- non-increasing function
- nonlinear function
- normalized Gaussian radial basis function
- normalized radial basis functions with equal heights
- normalized radial basis functions with equal volumes
- normalized radial basis functions with equal widths and heights
- normalized radial basis functions with equal widths
- normalized radial basis functions with unequal widths and heights
- objective function
- one-one function
- one-to-one function
- one-way function
- one-way hash function
- open-loop transfer function
- optical transfer function
- ordinary Gaussian radial basis function
- ordinary radial basis functions with equal widths
- ordinary radial basis functions with unequal widths
- orthogonal functions
- overlapped functions
- partial autocorrelation function
- penalty function
- perfect hash-function
- phase transfer function
- photoelectric work function
- photopic response function
- piecewise constant function
- piecewise linear function
- piecewise polynomial function
- Pierce function
- point-spread function
- polynomial function
- positive linear function
- postsynaptic potential function
- power function of test
- power function
- predefined function
- predicate function
- probability density function
- probability function
- probability mass function
- production function
- projection function
- projective function
- propagation function
- propositional function
- PSP function
- pulsating function
- pure virtual function
- quadratic error function
- radial basis function
- radial combination function
- ramp function
- range weighting function
- reactance function
- register function
- regression function
- resolvent function
- response function
- restricted function
- risk function
- saturating linear function
- scalar function
- scaling function
- scattering function
- scedastic function
- Schrödinger wave function
- scrambling function
- screen size-viewing distance function
- self-inverse function
- semilinear function
- sensing function
- sentential function
- shape function
- sigmoid activation function
- sigmoid function
- sign function
- signal function
- signum activation function
- signum function
- smooth function
- socket library function
- softmax activation function
- spectral density function
- spectral function
- spectral radiance function
- spline function
- spot function
- spread function
- square-integrable function
- square-law transfer function
- squashed sign function
- squashing function
- state function
- state query function
- steering function
- step function
- stream function
- summing function
- support entity function
- support function
- supported function
- surjection function
- surjective function
- survival function
- switch function
- switching function
- switch-type function
- symmetric saturating linear function
- tame function
- tan-sigmoid activation function
- target function
- tensor function
- tesseral function
- testing function
- tetragamma function
- thermionic work function
- threshold function
- through transfer function
- transcendental function
- transfer function
- trial function
- trigamma function
- trigonometric function
- tri-state function
- typematic function
- unate function
- uncurried function
- unit impulse function
- unit step function
- unsupported function
- user-defined function
- utility function
- vector function
- virtual function
- visibility function
- voltage potential function
- voltage transfer function
- Walsh functions
- wave function
- wave-number limited function
- weighting function
- window function
- work functionThe New English-Russian Dictionary of Radio-electronics > function
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73 programming
1) вчт. программирование•- applications programming
- automatic programming
- bare metal programming
- beam programming
- bottom-up programming
- business programming
- cascade programming
- compositional parallel programming
- compromise programming
- conceptual programming
- concurrent programming
- configuration programming
- declarative programming
- distributed logic programming
- dynamic programming
- egoless programming
- event-driven programming
- flow programming
- functional programming
- fuzzy programming
- generic programming
- genetic programming
- geometric programming
- goal-driven programming
- heuristic programming
- high-level language programming
- imperative programming
- inference programming
- integer programming
- interactive programming
- interpretive programming
- linear integer programming
- linear programming
- logic programming
- macro programming
- maintenance programming
- manual programming
- mathematical programming
- metalevel programming
- mixed integer programming
- modular parallel programming
- modular programming
- molecular programming
- multicriteria programming
- neurodynamic programming
- neurolinguistic programming
- nonlinear programming
- nonnumeric programming
- object-based programming
- object-language programming
- object-oriented programming
- off-line programming
- optimum programming
- parallel programming in transputer medium
- parallel programming with coordination structures
- parallel programming
- paranoid programming
- polynomial programming
- possibilistic programming
- predicate programming
- procedure-oriented programming
- production programming
- quadrature programming
- recursive quadratic programming
- stochastic programming
- structural programming
- structured programming
- switchboard programming
- symbolic programming
- symbolic-language programming
- system programming
- systems programming
- test programming
- top-down programming
- traditional programming
- typematic delay programming
- typematic rate programming
- visual programmingThe New English-Russian Dictionary of Radio-electronics > programming
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74 first-order
первопорядковый first-order phase transition ≈ физ. переход фазовый первого рода - first-order accuracy - first-order derivative - first-order design - first-order difference - first-order equation - first-order estimation - first-order filter - first-order hierarchy - first-order infinitesimal - first-order interaction - first-order jackknife - first-order language - first-order logic - first-order model - first-order oblateness - first-order predicate - first-order reaction - first-order sentence - first-order smoothing - first-order theor первого порядкаБольшой англо-русский и русско-английский словарь > first-order
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75 two-valued
двузначный property of being two-valued ≈ матем. двузначность two-valued predicate calculus ≈ двузначное [классическое] исчисление предикатов two-valued propositional calculus ≈ двузначное [классическое] исчисление высказываний, двузначное [классическое] пропозициональное исчисление - two-valued algebra - two-valued homomorphism - two-valued logic - two-valued measure - two-valued model - two-valued population - two-valued property - two-valued representation - two-valued table - two-valued theor (логика) двузначный (о логике) ;
имеющий истинностные значения "истина" и "ложь"Большой англо-русский и русско-английский словарь > two-valued
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76 symbol
1) символа) знакб) условный знак; условное обозначение; графическое обозначениев) вчт идентификаторг) образ; отображениед) эмблема2) представлять в символической форме; применять символическую запись; использовать символ(ы); использоваться в качестве символа3) использовать условные знаки или условные обозначения; использоваться в качестве условного знака или условного обозначения•- abstract symbol
- active symbol
- additional symbol
- admissible symbol
- aiming symbol
- algebraic symbol
- alpha symbol
- alphabetic symbol
- alphanumeric symbol
- annotation symbol
- auxiliary symbol
- barred symbol
- basic symbol
- blinking symbol
- Boolean symbol
- built-up symbol
- cell alphabet symbol
- check symbol
- checking symbol
- Christoffel symbol
- command symbol
- composite symbol
- connector symbol
- control symbol
- decision symbol
- definable symbol
- delta symbol
- delta Kronecker symbol
- derivative symbol
- digital symbol
- diode symbols
- dollar sign symbol
- dotted symbol
- euro sign symbol
- external symbol
- flowchart symbol
- flowcharting symbol
- functional symbol
- fundamental symbol
- generalized symbol
- generating symbol
- graphical symbol
- grouping symbol
- Hermann-Mauguin symbols
- illegal symbol
- information symbol
- input/output symbol
- international crystallographic symbols
- Kronecker symbols
- Levi-Civita symbols
- literal symbol
- logic symbol
- match-all symbol
- math symbol
- mathematical symbol
- metalogic symbol
- mnemonic symbol
- nonadmissible symbol
- nonblinking symbol
- nonterminal symbol
- numeric symbol
- odd symbol
- operator symbol
- partial derivative symbol
- phonemic symbol
- phonematic symbol
- predefined process symbol
- predicate symbol
- processing symbol
- proofreader's symbol
- punctuation symbol
- schematic symbol
- Schoenflies symbols
- separation symbol
- shading symbol
- Shubnikov symbols
- special symbol
- standard symbol
- start/stop symbol
- suggestive symbol
- syntactical symbol
- terminal symbol
- terminating symbol
- transistor symbols
- undeclared symbol
- undefined symbol
- underscore symbol
- unit symbol
- variable symbol
- vector symbol
- wildcard symbol
- wire symbol
- δ symbols -
77 symbol
1) символа) знакб) условный знак; условное обозначение; графическое обозначениев) вчт. идентификаторг) образ; отображениед) эмблема2) представлять в символической форме; применять символическую запись; использовать символ(ы); использоваться в качестве символа3) использовать условные знаки или условные обозначения; использоваться в качестве условного знака или условного обозначения•- abstract symbol
- active symbol
- additional symbol
- admissible symbol
- aiming symbol
- algebraic symbol
- alpha symbol
- alphabetic symbol
- alphanumeric symbol
- annotation symbol
- auxiliary symbol
- barred symbol
- basic symbol
- blinking symbol
- Boolean symbol
- built-up symbol
- cell alphabet symbol
- check symbol
- checking symbol
- Christoffel symbol
- command symbol
- composite symbol
- connector symbol
- control symbol
- decision symbol
- definable symbol
- delta Kronecker symbol
- delta symbol
- derivative symbol
- digital symbol
- diode symbols
- dollar sign symbol
- dotted symbol
- euro sign symbol
- external symbol
- flowchart symbol
- flowcharting symbol
- functional symbol
- fundamental symbol
- generalized symbol
- generating symbol
- graphical symbol
- grouping symbol
- Hermann-Mauguin symbols
- illegal symbol
- information symbol
- input/output symbol
- international crystallographic symbols
- Kronecker symbols
- Levi-Civita symbols
- literal symbol
- logic symbol
- match-all symbol
- math symbol
- mathematical symbol
- metalogic symbol
- mnemonic symbol
- nonadmissible symbol
- nonblinking symbol
- nonterminal symbol
- numeric symbol
- odd symbol
- operator symbol
- partial derivative symbol
- phonematic symbol
- phonemic symbol
- predefined process symbol
- predicate symbol
- processing symbol
- proofreader's symbol
- punctuation symbol
- schematic symbol
- Schoenflies symbols
- separation symbol
- shading symbol
- Shubnikov symbols
- special symbol
- standard symbol
- start/stop symbol
- suggestive symbol
- symbol of operator
- syntactical symbol
- terminal symbol
- terminating symbol
- transistor symbols
- undeclared symbol
- undefined symbol
- underscore symbol
- unit symbol
- variable symbol
- vector symbol
- wildcard symbol
- wire symbolThe New English-Russian Dictionary of Radio-electronics > symbol
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78 variable
1) переменная2) изменяемый; переменный; регулируемый•- additional variable
- allocated variable
- alphanumeric string variable
- alphameric string variable
- anonimous variable
- apparent variable
- artificial variable
- attached variable
- automatic variable
- auxiliary variable
- based variable
- basic variable
- Boolean variable
- bound variable
- chance variable
- character variable
- compile time variable
- complemented variable
- conditional variable
- control variable
- controlled variable
- decision variable
- dependent variable
- design variable
- digital variable
- dummy variable
- element variable
- endogenous variable - exogenous variable
- file-name variable
- file variable
- fixed variable
- free variable
- fuzzy variable
- global variable
- independent variable
- induction variable
- input variable - key variable
- label variable
- local variable
- logical variable
- logic variable
- loop-control variable
- loop variable
- main variable
- manipulated variable
- master variable
- metalinguistic variable
- missing variable
- morphic variable
- multicharacter variable
- mutually independent variables
- noncontrollable variable
- normalized variable
- notation variable
- operator variable
- output variable
- pointer variable
- predicate variable
- private variable
- process variable
- quantified variable
- random variable
- real variable
- regulated variable
- scalar variable
- selected variable
- shared variable
- simple variable
- slack variable
- slave variable
- state variable
- statement label variable
- status variable
- stochastic variable
- structure variable
- subscripted variable
- switching variable
- switch variable
- syntactic variable
- system variable
- task variable
- temporary variable
- ternary-valued variable
- top variable
- two-state variable
- two-valued variable
- unassigned variable
- unbound variable
- uncomplemented variable
- uncontrollable variable
- undeclared variable
- undefined variable
- uninitialized variable
- unregulated variable
- unrestricted variableEnglish-Russian dictionary of computer science and programming > variable
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79 quantification theory
формальная система логики, известная также под названием "исчисление предикатов" (predicate calculus) (см.также first-order logic)Англо-русский толковый словарь терминов и сокращений по ВТ, Интернету и программированию. > quantification theory
См. также в других словарях:
Predicate logic — In mathematical logic, predicate logic is the generic term for symbolic formal systems like first order logic, second order logic, many sorted logic or infinitary logic. This formal system is distinguished from other systems in that its formulas… … Wikipedia
Predicate (logic) — Sometimes it is inconvenient or impossible to describe a set by listing all of its elements. Another useful way to define a set is by specifying a property that the elements of the set have in common. The notation P(x) is used to denote a… … Wikipedia
predicate logic — noun a) The generic term for symbolic formal systems like first order logic, second order logic, many sorted logic or infinitary logic. b) First order logic. <! See . See Also: predicate calculus … Wiktionary
monadic predicate logic — noun The fragment of predicate logic in which all predicate letters are monadic (that is, they take only one argument), and there are no function letters … Wiktionary
predicate calculus — Logic. See functional calculus. Also called predicate logic. [1945 50] * * * Part of modern symbolic logic which systematically exhibits the logical relations between propositions involving quantifiers such as all and some. The predicate calculus … Universalium
Logic programming — is, in its broadest sense, the use of mathematical logic for computer programming. In this view of logic programming, which can be traced at least as far back as John McCarthy s [1958] advice taker proposal, logic is used as a purely declarative… … Wikipedia
Logic and the philosophy of mathematics in the nineteenth century — John Stillwell INTRODUCTION In its history of over two thousand years, mathematics has seldom been disturbed by philosophical disputes. Ever since Plato, who is said to have put the slogan ‘Let no one who is not a geometer enter here’ over the… … History of philosophy
Logic (disambiguation) — Logic is the study of the principles and criteria of valid inference and demonstration.Logic may also refer to:In logic and mathematics*A branch of logic: **Inductive logic, also called induction or inductive reasoning **Informal logic, the study … Wikipedia
Logic in computer science — describes topics where logic is applied to computer science and artificial intelligence. These include:*Investigations into logic that are guided by applications in computer science. For example: Combinatory logic and Abstract interpretation;… … Wikipedia
Predicate — or predication may refer to:*Predicate (mathematics), a relation, or the boolean valued characteristic function or indicator function of a relation *Predicate (logic), a fundamental concept in first order logic **in Bertrand Russell s theory of… … Wikipedia
Logic form — Logic forms are simple, first order logic knowledge representations of natural language sentences formed by the conjunction of concept predicates related through shared arguments. Each noun, verb, adjective, adverb, pronoun, preposition and… … Wikipedia