-
1 elementary predicate
1) Математика: элементарный предикат2) Лингвистика: атомарный предикат -
2 elementary predicate
English-Russian scientific dictionary > elementary predicate
-
3 predicate
логика1) предикат, сказуемое || предикатный2) утверждение || утверждать• -
4 элементарный предикат
elementary predicate мат.Русско-английский научно-технический словарь Масловского > элементарный предикат
-
5 атомарный предикат
Linguistics: atomic predicate, elementary predicateУниверсальный русско-английский словарь > атомарный предикат
-
6 элементарный предикат
1) Mathematics: elementary predicate2) Linguistics: atomic predicateУниверсальный русско-английский словарь > элементарный предикат
-
7 элементарный предикат
Большой англо-русский и русско-английский словарь > элементарный предикат
-
8 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
-
9 logic
-
10 лежать в основе
•The basis for the antibacterial effects of dyes is their ability to...
•Behind the Mullard invention is the notion that...
•Central to the theory is...
•Movement of charged particles in a magnetic field also forms (or constitutes) the basis for mass spectrometry.
•These equations form the basis (or foundation) of the theory of...
•These theories form (or provide) the foundation for (or are at the basis of) colour television.
•It is this form that provides the basis (or is fundamental) for a wide variety of TV antennas.
•Let us consider the physical conditions that underlie the Sun's magnetism.
•Boolean algebra underlies the theory of relations.
•Microcomputers are at the heart of "transaction" telephones for checking customers' credit.
•The general rule that the forces between two particles result from an exchange of other particles is basic to much of our present understanding of elementary-particle interactions.
•This distinction between electricity and magnetism is at the heart (or root) of the theory of...
•The Periodic Table provides the framework for the whole study of inorganic chemistry.
•Thermochemistry is basic to the study of chemical bonding.
•An understanding of dye laser operation is a building block for understanding the principles of other tunable laser systems.
•These interrelations are the heart of hydrodynamics.
•The chapter describes the fundamental physics that gives rise to the behaviour of the single junction and the transistor as circuit elements.
•The nature of energy lies at the heart of the mystery of our existence.
•This reaction is the basis for the cyanamide process for...
* * *Лежать в основе -- to be at the heart of, to lie at the heart of; to be at the root of; to be at the basis of, to form the basis of, to form the basis for; to form the foundation for; to underlieInference is al the heart of both language understanding and language generation.An accurate determination of primary flowrate lies at the heart of any turbine acceptance test.It is this particular approach that forms the basis of the dynamic testing technique described in this paper.Suppose that a single predicate underlies part of the meaning of each of the following sentences. (... лежит в основе части значения...)Русско-английский научно-технический словарь переводчика > лежать в основе
-
11 domain
1) владение, имение2) территория, зона3) физ. домен4) область, сфера5) матем. область определения6) область, домен• -
12 form
1) анкета; бланк2) вид; форма || придавать вид или форму3) контур; очертание4) конфигурация6) строит. опалубка; элемент опалубки7) скамейка, лавочка8) формуляр9) составлять; образовывать10) формироваться•calculation in a series form — матем. вычисление с помощью ряда
evaluation of indeterminate form — матем. раскрытие неопределённости
fraction in a factored form — матем. дробь в форме разложения на множители
in an expanded form — в виде ряда; в развёрнутом виде
integration in a closed form — матем. интегрирование в конечном виде
of closed form — матем. в конечном виде, с конечным числом членов
preparation of type form — полигр. чернение набора
reduction to a normal form — матем. приведение к нормальной форме
to bring into a canonical form — матем. приводить к канонической форме; приводить к каноническому виду
to form a circle — замыкаться в кольцо; образовывать кольцо
to rearrange in the form — переписывать в виде; преобразовывать к виду ( об уравнениях)
- absolutely convergent form - absolutely extreme form - definite form - elementary form - elimination form of inverse - everywhere regular form - evolutionary operation form - geodesic curvature form - indefinite form - p-adically equivalent form - relatively bounded form - repair request form - third fundamental form - totally definite form - totally discontinuous formto take on a form — принимать форму; принимать вид
-
13 formula
1) рецепт2) формула || формульный3) формулировка•- arithmetically valid formula - back interpolation formula - computational formula - computing formula - corrector formula - formally decidable formula - formally refutable formula - formula of finite increments - geometrically valid formula - homomorphically stable formula - identically false formula - identically true formula - intuitionistically valid formula - logically valid formula - maximum likelihood formula - modally valid formula - monotonically increasing formula - numeralwise expressible formula - numeralwise representable formula - positively valid formula - primitively recursive formula - reciprocity formula - secondarily valid formula - square stable formula - universally valid formulato follow the formula — подчиняться [следовать] формуле; описываться формулой
-
14 variable
1) переменная (величина) || переменный2) изменчивый3) изменяемый; варьируемый4) регулируемый•variable unrestricted in sign — переменная, не ограниченная в знаке
- absolutely integrable variable - anonymous free variable - complex free variable - complex random variable - discontinuous variable - discrete random variable - discrete variable variable - discrete variable - essentially free variable - excessive random variable - exchangeable random variables - generalized random variable - geometric random variable - infinitesimal random variable - jointly normal random variables - linguistic random variable - multinomial random variable - multinormal random variable - multiplicative random variable - mutually independent random variables - nonanticipative random variable - normed random variable - number variable - optimal stopping variable - orthonormal random variables - pairwise independent random variables - spatial variable - symmetrized random variable - two-state variable - two-valued variable - uniformly limited variableto separate variables — мат. разделять переменные
-
15 bottom
1. n низ, нижняя часть; конец2. n суть, основа, основаниеat bottom — в основе; по сути
down to rock bottom — до основания, полностью
3. n дно, днищеbottom door — трап, люк
4. n мор. днище; подводная часть корабляbottom dumping — выгрузка через днище, донная разгрузка
5. n мор. судноgoods imported in British bottoms — товар, ввезённый на британских судах
6. n мор. сиденье7. n разг. зад8. n разг. задняя часть брюк9. n разг. запас жизненных сил, выносливость; крепость10. n разг. ложе реки11. n разг. стр. основание, фундамент12. n разг. метал. под13. n разг. геол. постель, грунт, почва; подстилающая порода14. n разг. текст. грунт, протрава, закрепитель красителя15. n разг. басовые или баритональные инструменты в оркестреfrom the bottom of the heart — из глубины души, от всего сердца
to knock the bottom out of smth. — выбить почву из-под ног; подорвать, опровергнуть, свести на нет
bin bottom — днище бункера, силоса или закрома
16. a самый нижний17. a последний, крайний18. a находящийся на дне19. a служащий причиной; лежащий в основе20. v приделывать дно, низ, сиденье21. v касаться дна; измерять глубину22. v добираться до сути, искать причину; вникать, понимать23. v обыкн. основывать; давать обоснование24. v книжн. основываться25. v тех. грунтоватьСинонимический ряд:1. bottommost (adj.) bottommost; lowermost; lowest; nethermost; rock-bottom; undermost2. fundamental (adj.) basal; basic; conclusive; determining; elementary; final; foundational; fundamental; last; original; primary; radical; underlying3. base (noun) base; basement; basis; bed; bedrock; core; footing; foundation; ground; groundwork; hardpan; heart; infrastructure; rest; seating; substratum; substruction; substructure; support; underpinning; understructure4. buttocks (noun) backside; beam; behind; breech; buttocks; cheeks; derriere; fanny; fundament; hams; haunches; heinie; hind end; hiney; hunkers; nates; podex; posterior; rear; rear end; rump; seat; tail; tail end5. essence (noun) be-all and end-all; essence; essentiality; marrow; pith; quintessence; quintessential; rock bottom; root; soul; stuff; substance; virtuality6. lowest part (noun) depths; foot; lowest part; nadir; sole; underneath; underside; undersurface7. base (verb) base; establish; found; ground; predicate; rest; stayАнтонимический ряд:
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
metalogic — /met euh loj ik/, n. the logical analysis of the fundamental concepts of logic. [1835 45; META + LOGIC] * * * Study of the syntax and the semantics of formal languages and formal systems. It is related to, but does not include, the formal… … Universalium
First-order logic — is a formal logical system used in mathematics, philosophy, linguistics, and computer science. It goes by many names, including: first order predicate calculus, the lower predicate calculus, quantification theory, and predicate logic (a less… … Wikipedia
Principia Mathematica — For Isaac Newton s book containing basic laws of physics, see Philosophiæ Naturalis Principia Mathematica. The title page of the shortened version of the Principia Mathematica to *56. The Principia Mathematica is a three volume work on the… … Wikipedia
Function (mathematics) — f(x) redirects here. For the band, see f(x) (band). Graph of example function, In mathematics, a function associates one quantity, the a … Wikipedia
Interpretation (logic) — An interpretation is an assignment of meaning to the symbols of a formal language. Many formal languages used in mathematics, logic, and theoretical computer science are defined in solely syntactic terms, and as such do not have any meaning until … Wikipedia
Vienna Circle — The Vienna Circle (in German: der Wiener Kreis) was a group of philosophers who gathered around Moritz Schlick when he was called to the Vienna University in 1922, organized in a philosophical association named Verein Ernst Mach (Ernst Mach… … Wikipedia
formal logic — the branch of logic concerned exclusively with the principles of deductive reasoning and with the form rather than the content of propositions. [1855 60] * * * Introduction the abstract study of propositions, statements, or assertively used … Universalium
Logicism — is one of the schools of thought in the philosophy of mathematics, putting forth the theory that mathematics is an extension of logic and therefore some or all mathematics is reducible to logic.[1] Bertrand Russell and Alfred North Whitehead… … Wikipedia
Atomic sentence — In logic, an atomic sentence is a type of declarative sentence which is either true or false (may also be referred to as a proposition, statement or truthbearer) and which cannot be broken down into other simpler sentences. For example The dog… … Wikipedia
logic, philosophy of — Philosophical study of the nature and scope of logic. Examples of questions raised in the philosophy of logic are: In virtue of what features of reality are the laws of logic true? ; How do we know the truths of logic? ; and Could the laws of… … Universalium
