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empirical logic

Толкование Перевод

1 empirical logic

Математика: эмпирическая логика

Универсальный англо-русский словарь > empirical logic
2 empirical logic

мат.

эмпирическая логика

English-Russian scientific dictionary > empirical logic
3 empirical logic

эмпирическая логика

Англо-русский словарь по исследованиям и ноу-хау > empirical logic
4 logic

1) логика || логический

2) вчт логические схемы, логика

3) логический блок; логический узел

•

logic with identity — логика с тождеством

logic without identity — логика без тождества

- algebraic logic

- applied logic

- auxiliary logic

- bivalent logic

- cellular logic

- class logic

- classical logic

- classical propositional logic

- commodity logic

- continuous logic

- countably complete logic

- decision logic

- decoding logic

- deductive logic

- derivative logic

- deterministic logic

- dialectical logic

- digitizing logic

- dual logic

- dyadic logic

- dynamic logic

- elementary logic

- empirical logic

- equational logic

- extended logic

- extensional logic

- external logic

- feature logic

- finite-valued logic

- first-order logic

- free logic

- fuzzy logic

- general modal logic

- generalized logic

- generalized stochastic logic

- heuristic logic

- imperative logic

- implicational logic

- inductive logic

- inductively generated logic

- infinitary logic

- infinite-valued logic

- intensional logic

- intermediate logic

- intrinsic logic

- intuitionistic logic

- iterative logic

- lattice-theoretic logic

- limit logic

- logic of certainty

- logic of evidence

- logic of existence

- logic of higher order

- logic of modalities

- logic of norms

- logic of part

- logic of preference

- logic of strict implication

- logic of truth

- logic of verification

- logic of whole

- minimal logic

- modal logic

- monadic logic

- multivalued logic

- natural logic

- nonclassical logic

- nonconstructive logic

- nonmonotonic logic

- normative logic

- n-valued logic

- object logic

- orthodox logic

- polyvalent logic

- positive logic

- predicate logic

- projectively generated logic

- propositional logic

- pure logic

- quantificational logic

- quite full logic

- reduced logic

- redundant logic

- search logic

- semicomplete logic

- sentential logic

- separable logic

- set-theoretic predicate logic

- shielded

- situational logic

- static logic

- stochastic logic

- strict logic

- strongly strict logic

- superintuitionistic logic

- switching logic

- syllogistic logic

- system logic

- technical logic

- tense logic

- three-level logic

- three-valued logic

- topological logic

- traditional logic

- transforming logic

- trivalent logic

- unordered logic

- unorthodox logic

- unspecialized logic

English-Russian scientific dictionary > logic
5 Logic

   1) The Science of Logic Finds Ordinary Language to Be an Obstacle

   My initial step... was to attempt to reduce the concept of ordering in a sequence to that of logical consequence, so as to proceed from there to the concept of number. To prevent anything intuitive from penetrating here unnoticed, I had to bend every effort to keep the chain of inference free of gaps. In attempting to comply with this requirement in the strictest possible way, I found the inadequacy of language to be an obstacle. (Frege, 1972, p. 104)

   2) Logic Is a Microscope

   I believe I can make the relation of my 'conceptual notation' to ordinary language clearest if I compare it to the relation of the microscope to the eye. The latter, because of the range of its applicability and because of the ease with which it can adapt itself to the most varied circumstances, has a great superiority over the microscope. Of course, viewed as an optical instrument it reveals many imperfections, which usually remain unnoticed only because of its intimate connection with mental life. But as soon as scientific purposes place strong requirements upon sharpness of resolution, the eye proves to be inadequate.... Similarly, this 'conceptual notation' is devised for particular scientific purposes; and therefore one may not condemn it because it is useless for other purposes. (Frege, 1972, pp. 104-105)

   3) Logic Carries on an Unceasing Struggle with Psychology

   To sum up brieﬂy, it is the business of the logician to conduct an unceasing struggle against psychology and those parts of language and grammar which fail to give untrammeled expression to what is logical. He does not have to answer the question: How does thinking normally take place in human beings? What course does it naturally follow in the human mind? What is natural to one person may well be unnatural to another. (Frege, 1979, pp. 6-7)

   4) Logic Should Replace the Ordinary Language of Everyday Discourse

   We are very dependent on external aids in our thinking, and there is no doubt that the language of everyday life-so far, at least, as a certain area of discourse is concerned-had first to be replaced by a more sophisticated instrument, before certain distinctions could be noticed. But so far the academic world has, for the most part, disdained to master this instrument. (Frege, 1979, pp. 6-7)

   5) Logic Is Unnatural

   There is no reproach the logician need fear less than the reproach that his way of formulating things is unnatural.... If we were to heed those who object that logic is unnatural, we would run the risk of becoming embroiled in interminable disputes about what is natural, disputes which are quite incapable of being resolved within the province of logic. (Frege, 1979, p. 128)

   6) The Significance of " Baby Logic" for Linguistics

   [L]inguists will be forced, internally as it were, to come to grips with the results of modern logic. Indeed, this is apparently already happening to some extent. By "logic" is not meant here recursive function-theory, California model-theory, constructive proof-theory, or even axiomatic settheory. Such areas may or may not be useful for linguistics. Rather under "logic" are included our good old friends, the homely locutions "and," "or," "if-then," "if and only if," "not," "for all x," "for some x," and "is identical with," plus the calculus of individuals, event-logic, syntax, denotational semantics, and... various parts of pragmatics.... It is to these that the linguist can most profitably turn for help. These are his tools. And they are "clean tools," to borrow a phrase of the late J. L. Austin in another context, in fact, the only really clean ones we have, so that we might as well use them as much as we can. But they constitute only what may be called "baby logic." Baby logic is to the linguist what "baby mathematics" (in the phrase of Murray Gell-Mann) is to the theoretical physicist-very elementary but indispensable domains of theory in both cases. (Martin, 1969, pp. 261-262)

   7) The Existence of a Mental Logic Denied

   There appears to be no branch of deductive inference that requires us to assume the existence of a mental logic in order to do justice to the psychological phenomena. To be logical, an individual requires, not formal rules of inference, but a tacit knowledge of the fundamental semantic principle governing any inference; a deduction is valid provided that there is no way of interpreting the premises correctly that is inconsistent with the conclusion. Logic provides a systematic method for searching for such counter-examples. The empirical evidence suggests that ordinary individuals possess no such methods. (Johnson-Laird, quoted in Mehler, Walker & Garrett, 1982, p. 130)

   8) The Fundamental Paradox of Logic

   The fundamental paradox of logic [that "there is no class (as a totality) of those classes which, each taken as a totality, do not belong to themselves" (Russell to Frege, 16 June 1902, in van Heijenoort, 1967, p. 125)] is with us still, bequeathed by Russell-by way of philosophy, mathematics, and even computer science-to the whole of twentieth-century thought. Twentieth-century philosophy would begin not with a foundation for logic, as Russell had hoped in 1900, but with the discovery in 1901 that no such foundation can be laid. (Everdell, 1997, p. 184)

Historical dictionary of quotations in cognitive science > Logic
6 эмпирическая логика

мат. empirical logic

Большой англо-русский и русско-английский словарь > эмпирическая логика
7 Artificial Intelligence

   1) Programs and the Complexity of Human Mental Processes

   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)

   2) Artificial Intelligence Is an Engineering Discipline

   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)

   3) A Sceptical View of Artificial Intelligence

   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, Eventually

   Just 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 Program

   Many 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)

   6) The Meaning of a Symbolic Description

   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)

   7) The Principle of Artificial Intelligence

   [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)

   8) Artificial Intelligence Recognizes the Need for Knowledge in Its Systems

   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 Form

   The 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)

    10) There Are Many Types of Reasoning

   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)

    11) Programs Are Beginning to Do Things That Critics Have Asserted to Be Impossible

   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)

    12) The Synthesis of Man and Machine

   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)

    13) The Thesis of Good Old-Fashioned Artificial Intelligence (GOFAI)

   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 Formation

   It 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)

    15) Four Kinds of Artificial Intelligence

   We might distinguish among four kinds of AI.

   ♦ Nonpsychological 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.

   ♦ Weak Psychological AI

   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.

   ♦ Strong Psychological AI

... 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)

   ♦ Suprapsychological AI

   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 Contexts

   Even 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)

    17) Form and Content Are Not Fundamentally Different

   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)

    18) The Assumption That the Mind Is a Formal System

   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 Intelligence

   The 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)

    20) Mathematical Logic Provides the Basis for Theory in AI

   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 Propositions

   In 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)

    22) Definitions of Artificial Intelligence

   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)

    23) The Computer Can Not Be a Model of the Mind

   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)

    24) Computers Will Not Always Be Inferior to Human Brains

   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
8 Logical Positivism

    The Question of the Validity and Justification of Metaphysics

   There have been many opponents of metaphysics from the Greek sceptics to the empiricists of the nineteenth century. Criticisms of very diverse kinds have been set forth. Many have declared that the doctrine of metaphysics is false, since it contradicts our empirical knowledge. Others have believed it to be uncertain, on the ground that its problems transcend the limits of human knowledge. Many anti-metaphysicians have declared that occupation with metaphysical questions is sterile. Whether or not these questions can be answered, it is at any rate unnecessary to worry about them; let us devote ourselves entirely to the practical tasks which confront active men every day of their lives!

   The development of modern logic has made it possible to give a new and sharper answer to the question of the validity and justification of metaphysics. The researchers of applied logic or the theory of knowledge, which aim at clarifying the cognitive content of scientific statements and thereby the meanings of the terms that occur in the statements, by means of logical analysis, lead to a positive and to a negative result. The positive result is worked out in the domain of empirical science; the various concepts of the various branches of science are clarified; their formal, logical and epistemological connections are made explicit.

   In the domain of metaphysics, including all philosophy of value and normative theory, logical analysis yields the negative result that the al leged statements in this domain are entirely meaningless. Therewith a radical elimination of metaphysics is attained, which was not yet possible from the earlier anti-metaphysical standpoints. (Carnap, 1959, p. 60)

Historical dictionary of quotations in cognitive science > Logical Positivism
9 simulation

моделирование; имитационное моделирование; имитация

- analog simulation
- analog-computer simulation
- analog-digital simulation
- behavioral simulation
- cell-level simulation
- circuit simulation
- computer simulation
- conceptual data simulation
- continuous simulation
- critical-path timing simulation
- data simulation
- date simulation
- deterministic simulation
- digital simulation
- digital-computer simulation
- dynamic simulation
- electronic simulation
- empirical simulation
- environmental simulation
- event-driven logic simulation
- functional simulation
- gaming simulation
- gate-level logic simulation
- geometrical simulation
- hardware simulation
- heuristic simulation
- high-level simulation
- human factor simulation
- input/output simulation
- interrupt simulation
- logic simulation
- machine simulation
- macroscopic freeway simulation
- mathematical simulation
- matrix simulation
- mixed-level simulation
- mixed-mode simulation
- Monte-Carlo simulation
- multilevel simulation
- multilevel-mode simulation
- multimode simulation
- network simulation
- numerical simulation
- operational simulation
- physical simulation
- real-time simulation
- smart simulation
- software simulation
- space simulation
- stochastic simulation
- subcircuit-level simulation
- system simulation
- tactic combat simulation
- time simulation
- traffic simulation
- transistor-level simulation
- virtual reality simulation
- visual interactive simulation
- voice simulation

English-Russian electronics dictionary > simulation
10 simulation

моделирование; имитационное моделирование; имитация

- analog simulation
- analog-computer simulation
- analog-digital simulation
- behavioral simulation
- cell-level simulation
- circuit simulation
- computer simulation
- conceptual data simulation
- continuous simulation
- critical-path timing simulation
- data simulation
- date simulation
- deterministic simulation
- digital simulation
- digital-computer simulation
- dynamic simulation
- electronic simulation
- empirical simulation
- environmental simulation
- event-driven logic simulation
- functional simulation
- gaming simulation
- gate-level logic simulation
- geometrical simulation
- hardware simulation
- heuristic simulation
- high-level simulation
- human factor simulation
- input/output simulation
- interrupt simulation
- logic simulation
- machine simulation
- macroscopic freeway simulation
- mathematical simulation
- matrix simulation
- mixed-level simulation
- mixed-mode simulation
- Monte-Carlo simulation
- multilevel simulation
- multilevel-mode simulation
- multimode simulation
- network simulation
- numerical simulation
- operational simulation
- physical simulation
- real-time simulation
- smart simulation
- software simulation
- space simulation
- stochastic simulation
- subcircuit-level simulation
- system simulation
- tactic combat simulation
- time simulation
- traffic simulation
- transistor-level simulation
- virtual reality simulation
- visual interactive simulation
- voice simulation

The New English-Russian Dictionary of Radio-electronics > simulation
11 analysis

анализ

- analysis of covariance

- analysis of means
- analysis of observations
- analysis of optical spectrum
- analysis of variance

- 2,5D analysis of interconnect
- activation analysis
- a-posteriori analysis
- approximate analysis
- a-priori analysis
- automatic number analysis
- batch circuit analysis
- behavioral analysis
- binding-time analysis
- bottom-up analysis
- cepstral analysis
- cipher analysis
- circuit analysis
- cluster analysis
- combinatorial analysis
- comparative analysis
- compatibility analysis
- complex analysis
- content analysis
- contingency analysis
- conversational analysis
- cost analysis
- cost/benefit analysis
- covariance analysis
- critical path analysis
- crystal analysis
- cyclic analysis
- dataflow analysis
- decision-tree analysis
- dimensional analysis
- discourse analysis
- discriminant analysis
- display data analysis
- domain analysis
- EDX analysis
- electron diffraction analysis
- electron probe analysis
- empirical analysis
- energy-dispersive X-ray analysis
- error analysis
- factor analysis
- failure analysis
- failure mode and effects analysis

- fault-tree analysis

- feature analysis

- finite element analysis

- flow analysis
- fluorescence analysis
- Fourier analysis
- fractal image analysis
- frequency analysis
- frequency-domain analysis
- frequency-response analysis
- functional analysis
- fuzzy analysis
- fuzzy logic analysis
- harmonic analysis
- incremental circuit analysis
- interactive signal analysis
- interferometric analysis
- interval analysis
- joint analysis
- Kaplan-Meier analysis
- kernel discriminant analysis
- k-means cluster analysis
- large-signal analysis
- laser microprobe analysis
- linear two-group discriminant analysis
- linguistic analysis
- logic analysis
- logistic analysis
- logit analysis
- log-linear analysis
- luminescent analysis
- magnetic neutron diffraction analysis
- malfunction analysis
- mathematical analysis
- matrix analysis
- maximum-likelihood analysis
- means/ends analysis
- memory operating characteristic analysis
- mesh analysis
- meta-analysis
- microprobe analysis
- mixed-level analysis
- mixed-mode analysis
- modified nodal analysis
- Monte-Carlo analysis
- morphological analysis
- multifactor analysis of variance
- multilevel analysis
- multimode analysis
- multiple discriminant analysis
- multivariate analysis
- network analysis
- nodal analysis
- numerical analysis
- object-oriented analysis

- off-line circuit analysis
- operation analysis
- path analysis
- phase-plane analysis
- photon analysis
- photothermoelectric analysis
- policy analysis
- predictable failure analysis

- principal components analysis
- probabilistic analysis
- problem analysis
- protocol analysis
- qualitative analysis
- quantitative analysis
- radar signal analysis
- radiographic analysis
- radiometric analysis
- randomized block analysis of variance
- receiver operating characteristic analysis
- regression analysis
- regression correlation analysis
- repeated measures analysis of variance
- requirements analysis
- risk analysis
- sampling analysis
- set analysis
- signature analysis
- single-mode analysis
- small-signal analysis
- sound analysis
- sparse table analysis
- spectral analysis
- spectrophotometric analysis
- spectrum signature analysis
- speech analysis
- static analysis
- statistical analysis
- sticky analysis
- structural analysis
- structured analysis
- structured systems analysis
- survival analysis
- syntactic analysis
- syntactical analysis
- system analysis
- system analysis in control
- tensor analysis
- time-domain analysis
- time-to-event analysis
- top-down analysis
- topological analysis
- traffic analysis
- trend analysis
- two-factor factorial analysis of variance
- wave-length dispersive X-ray analysis
- weighted analysis
- what if analysis
- worst-case analysis
- X-ray analysis
- X-ray spectral analysis
- X-ray structure analysis

English-Russian electronics dictionary > analysis
12 analysis

анализ

- 2,5D analysis of interconnect
- a posteriori analysis
- a priori analysis
- activation analysis
- analysis of covariance
- analysis of means
- analysis of observations
- analysis of optical spectrum
- analysis of variance
- approximate analysis
- automatic number analysis
- batch circuit analysis
- behavioral analysis
- binding-time analysis
- bottom-up analysis
- cepstral analysis
- cipher analysis
- circuit analysis
- cluster analysis
- combinatorial analysis
- comparative analysis
- compatibility analysis
- complex analysis
- content analysis
- contingency analysis
- conversational analysis
- cost analysis
- cost/benefit analysis
- covariance analysis
- critical path analysis
- crystal analysis
- cyclic analysis
- dataflow analysis
- decision-tree analysis
- dimensional analysis
- discourse analysis
- discriminant analysis
- display data analysis
- domain analysis
- EDX analysis
- electron diffraction analysis
- electron probe analysis
- empirical analysis
- energy-dispersive X-ray analysis
- error analysis
- factor analysis
- failure analysis
- failure mode and effects analysis
- fault-tree analysis
- feature analysis
- finite element analysis
- flow analysis
- fluorescence analysis
- Fourier analysis
- fractal image analysis
- frequency analysis
- frequency-domain analysis
- frequency-response analysis
- functional analysis
- fuzzy analysis
- fuzzy logic analysis
- harmonic analysis
- incremental circuit analysis
- interactive signal analysis
- interferometric analysis
- interval analysis
- joint analysis
- Kaplan-Meier analysis
- kernel discriminant analysis
- k-means cluster analysis
- large-signal analysis
- laser microprobe analysis
- linear two-group discriminant analysis
- linguistic analysis
- logic analysis
- logistic analysis
- logit analysis
- log-linear analysis
- luminescent analysis
- magnetic neutron diffraction analysis
- malfunction analysis
- mathematical analysis
- matrix analysis
- maximum-likelihood analysis
- means/ends analysis
- memory operating characteristic analysis
- mesh analysis
- meta-analysis
- microprobe analysis
- mixed-level analysis
- mixed-mode analysis
- modified nodal analysis
- Monte-Carlo analysis
- morphological analysis
- multifactor analysis of variance
- multilevel analysis
- multimode analysis
- multiple discriminant analysis
- multivariate analysis
- network analysis
- nodal analysis
- numerical analysis
- object-oriented analysis
- off-line circuit analysis
- operation analysis
- path analysis
- phase-plane analysis
- photon analysis
- photothermoelectric analysis
- policy analysis
- predictable failure analysis
- principal components analysis
- probabilistic analysis
- problem analysis
- protocol analysis
- qualitative analysis
- quantitative analysis
- radar signal analysis
- radiographic analysis
- radiometric analysis
- randomized block analysis of variance
- receiver operating characteristic analysis
- regression analysis
- regression correlation analysis
- repeated measures analysis of variance
- requirements analysis
- risk analysis
- sampling analysis
- set analysis
- signature analysis
- single-mode analysis
- small-signal analysis
- sound analysis
- sparse table analysis
- spectral analysis
- spectrophotometric analysis
- spectrum signature analysis
- speech analysis
- static analysis
- statistical analysis
- sticky analysis
- structural analysis
- structured analysis
- structured systems analysis
- survival analysis
- syntactic analysis
- syntactical analysis
- system analysis in control
- system analysis
- tensor analysis
- time-domain analysis
- time-to-event analysis
- top-down analysis
- topological analysis
- traffic analysis
- trend analysis
- two-factor factorial analysis of variance
- wave-length dispersive X-ray analysis
- weighted analysis
- what if analysis
- worst-case analysis
- X-ray analysis
- X-ray spectral analysis
- X-ray structure analysis

The New English-Russian Dictionary of Radio-electronics > analysis
13 Memory

   1) A Unitary Search Process for All the Phenomena of Memory

   To what extent can we lump together what goes on when you try to recall: (1) your name; (2) how you kick a football; and (3) the present location of your car keys? If we use introspective evidence as a guide, the first seems an immediate automatic response. The second may require constructive internal replay prior to our being able to produce a verbal description. The third... quite likely involves complex operational responses under the control of some general strategy system. Is any unitary search process, with a single set of characteristics and inputoutput relations, likely to cover all these cases? (Reitman, 1970, p. 485)

   2) Semantic Memory Is a Mental Thesaurus

   [Semantic memory] Is a mental thesaurus, organized knowledge a person possesses about words and other verbal symbols, their meanings and referents, about relations among them, and about rules, formulas, and algorithms for the manipulation of these symbols, concepts, and relations. Semantic memory does not register perceptible properties of inputs, but rather cognitive referents of input signals. (Tulving, 1972, p. 386)

   3) The Development of Mnemonic Codes

   The mnemonic code, far from being fixed and unchangeable, is structured and restructured along with general development. Such a restructuring of the code takes place in close dependence on the schemes of intelligence. The clearest indication of this is the observation of different types of memory organisation in accordance with the age level of a child so that a longer interval of retention without any new presentation, far from causing a deterioration of memory, may actually improve it. (Piaget & Inhelder, 1973, p. 36)

   4) The Logic of Some Memory Theorization Is of Dubious Worth in the History of Psychology

   If a cue was effective in memory retrieval, then one could infer it was encoded; if a cue was not effective, then it was not encoded. The logic of this theorization is "heads I win, tails you lose" and is of dubious worth in the history of psychology. We might ask how long scientists will puzzle over questions with no answers. (Solso, 1974, p. 28)

   5) The Constituent Elements of Memory Theory

   We have iconic, echoic, active, working, acoustic, articulatory, primary, secondary, episodic, semantic, short-term, intermediate-term, and longterm memories, and these memories contain tags, traces, images, attributes, markers, concepts, cognitive maps, natural-language mediators, kernel sentences, relational rules, nodes, associations, propositions, higher-order memory units, and features. (Eysenck, 1977, p. 4)

   6) The Problem with the Memory Metaphor

   The problem with the memory metaphor is that storage and retrieval of traces only deals [ sic] with old, previously articulated information. Memory traces can perhaps provide a basis for dealing with the "sameness" of the present experience with previous experiences, but the memory metaphor has no mechanisms for dealing with novel information. (Bransford, McCarrell, Franks & Nitsch, 1977, p. 434)

   7) The Results of a Hundred Years of the Psychological Study of Memory Are Somewhat Discouraging

   The results of a hundred years of the psychological study of memory are somewhat discouraging. We have established firm empirical generalisations, but most of them are so obvious that every ten-year-old knows them anyway. We have made discoveries, but they are only marginally about memory; in many cases we don't know what to do with them, and wear them out with endless experimental variations. We have an intellectually impressive group of theories, but history offers little confidence that they will provide any meaningful insight into natural behavior. (Neisser, 1978, pp. 12-13)

   8) The Structure and Function of a Schema in Memory

   A schema, then is a data structure for representing the generic concepts stored in memory. There are schemata representing our knowledge about all concepts; those underlying objects, situations, events, sequences of events, actions and sequences of actions. A schema contains, as part of its specification, the network of interrelations that is believed to normally hold among the constituents of the concept in question. A schema theory embodies a prototype theory of meaning. That is, inasmuch as a schema underlying a concept stored in memory corresponds to the mean ing of that concept, meanings are encoded in terms of the typical or normal situations or events that instantiate that concept. (Rumelhart, 1980, p. 34)

   9) Competence and Performance in Theories of Memory

   Memory appears to be constrained by a structure, a "syntax," perhaps at quite a low level, but it is free to be variable, deviant, even erratic at a higher level....

   Like the information system of language, memory can be explained in part by the abstract rules which underlie it, but only in part. The rules provide a basic competence, but they do not fully determine performance. (Campbell, 1982, pp. 228, 229)

    10) Metaphors of Memory

   When people think about the mind, they often liken it to a physical space, with memories and ideas as objects contained within that space. Thus, we speak of ideas being in the dark corners or dim recesses of our minds, and of holding ideas in mind. Ideas may be in the front or back of our minds, or they may be difficult to grasp. With respect to the processes involved in memory, we talk about storing memories, of searching or looking for lost memories, and sometimes of finding them. An examination of common parlance, therefore, suggests that there is general adherence to what might be called the spatial metaphor. The basic assumptions of this metaphor are that memories are treated as objects stored in specific locations within the mind, and the retrieval process involves a search through the mind in order to find specific memories....

   However, while the spatial metaphor has shown extraordinary longevity, there have been some interesting changes over time in the precise form of analogy used. In particular, technological advances have inﬂuenced theoretical conceptualisations.... The original Greek analogies were based on wax tablets and aviaries; these were superseded by analogies involving switchboards, gramophones, tape recorders, libraries, conveyor belts, and underground maps. Most recently, the workings of human memory have been compared to computer functioning... and it has been suggested that the various memory stores found in computers have their counterparts in the human memory system. (Eysenck, 1984, pp. 79-80)

    11) Comparison of Primary Memory and Secondary Memory

   Primary memory [as proposed by William James] relates to information that remains in consciousness after it has been perceived, and thus forms part of the psychological present, whereas secondary memory contains information about events that have left consciousness, and are therefore part of the psychological past. (Eysenck, 1984, p. 86)

    12) Semantic Memory and Episodic Memory

   Once psychologists began to study long-term memory per se, they realized it may be divided into two main categories.... Semantic memories have to do with our general knowledge about the working of the world. We know what cars do, what stoves do, what the laws of gravity are, and so on. Episodic memories are largely events that took place at a time and place in our personal history. Remembering specific events about our own actions, about our family, and about our individual past falls into this category. With amnesia or in aging, what dims... is our personal episodic memories, save for those that are especially dear or painful to us. Our knowledge of how the world works remains pretty much intact. (Gazzaniga, 1988, p. 42)

    13) The Relation of Memory to Thinking

   The nature of memory... provides a natural starting point for an analysis of thinking. Memory is the repository of many of the beliefs and representations that enter into thinking, and the retrievability of these representations can limit the quality of our thought. (Smith, 1990, p. 1)

Historical dictionary of quotations in cognitive science > Memory
14 modeling

1) моделирование (1. создание упрощённого представления объекта, процесса или явления; использование структурной аналогии 2. макетирование 3. создание образца, эталона или шаблона 4. использование примера; отнесение к определённому типу) || модельный (1. относящийся к упрощённому представлению объекта, процесса или явления; использующий структурную аналогию 2. макетный 3. образцовый; эталонный; шаблонный 4. примерный; типовой)

2) выполнение функций модели

3) создание по образцу, эталону или шаблону

4) следование определённому стилю или выбранному дизайну

5) использование моделирующей схемы

•

- modeling of application domain
- modeling of consciousness
- modeling of database
- analog modeling
- analytical modeling
- causal modeling
- cognitive modeling
- computer modeling
- computer-aided modeling
- conceptual data modeling
- data modeling
- date modeling
- deterministic modeling
- digital modeling
- dynamic modeling
- empirical modeling
- functional modeling
- fuzzy modeling
- gaming modeling
- geometrical modeling
- hardware modeling
- heuristic modeling
- hidden Markov modeling
- hierarchical modeling
- high-level modeling
- information system modeling
- interconnect modeling
- knowledge modeling
- logic modeling
- long-term correlations modeling
- machine modeling
- magnetic hysteresis modeling
- mathematical modeling
- matrix modeling
- Monte Carlo modeling
- network modeling
- neurofuzzy adaptive modeling
- neuron network modeling
- numerical modeling
- object-oriented modeling
- on-line modeling
- physical modeling
- quasi-multidimensional modeling
- scale modeling
- simulation modeling
- smart modeling
- software modeling
- solid modeling
- solution-based modeling
- stochastic modeling
- surface modeling
- symbolic modeling
- synergetic modeling
- system modeling
- system-on-a-chip modeling
- virtual reality modeling
- visual interactive modeling

English-Russian electronics dictionary > modeling
15 modeling

1) моделирование (1. создание упрощённого представления объекта, процесса или явления; использование структурной аналогии 2. макетирование 3. создание образца, эталона или шаблона 4. использование примера; отнесение к определённому типу) || модельный (1. относящийся к упрощённому представлению объекта, процесса или явления; использующий структурную аналогию 2. макетный 3. образцовый; эталонный; шаблонный 4. примерный; типовой)

2) выполнение функций модели

3) создание по образцу, эталону или шаблону

4) следование определённому стилю или выбранному дизайну

5) использование моделирующей схемы

•

- analog modeling
- analytical modeling
- causal modeling
- cognitive modeling
- computer modeling
- computer-aided modeling
- conceptual data modeling
- data modeling
- date modeling
- deterministic modeling
- digital modeling
- dynamic modeling
- empirical modeling
- functional modeling
- fuzzy modeling
- gaming modeling
- geometrical modeling
- hardware modeling
- heuristic modeling
- hidden Markov modeling
- hierarchical modeling
- high-level modeling
- information system modeling
- interconnect modeling
- knowledge modeling
- logic modeling
- long-term correlations modeling
- machine modeling
- magnetic hysteresis modeling
- mathematical modeling
- matrix modeling
- modeling of application domain
- modeling of consciousness
- modeling of database
- Monte Carlo modeling
- network modeling
- neurofuzzy adaptive modeling
- neuron network modeling
- numerical modeling
- object-oriented modeling
- on-line modeling
- physical modeling
- quasi-multidimensional modeling
- scale modeling
- simulation modeling
- smart modeling
- software modeling
- solid modeling
- solution-based modeling
- stochastic modeling
- surface modeling
- symbolic modeling
- synergetic modeling
- system modeling
- system-on-a-chip modeling
- virtual reality modeling
- visual interactive modeling

The New English-Russian Dictionary of Radio-electronics > modeling
16 Thinking

   1) I Am a Thinking Thing

   But what then am I? A thing which thinks. What is a thing which thinks? It is a thing which doubts, understands, [conceives], affirms, denies, wills, refuses, which also imagines and feels. (Descartes, 1951, p. 153)

   2) Thinking Is Not Independent of the Expression of Thought

   I have been trying in all this to remove the temptation to think that there "must be" a mental process of thinking, hoping, wishing, believing, etc., independent of the process of expressing a thought, a hope, a wish, etc.... If we scrutinize the usages which we make of "thinking," "meaning," "wishing," etc., going through this process rids us of the temptation to look for a peculiar act of thinking, independent of the act of expressing our thoughts, and stowed away in some particular medium. (Wittgenstein, 1958, pp. 41-43)

   3) The Experimental Differentiation of Concrete and Propositional Operations

   Analyse the proofs employed by the subject. If they do not go beyond observation of empirical correspondences, they can be fully explained in terms of concrete operations, and nothing would warrant our assuming that more complex thought mechanisms are operating. If, on the other hand, the subject interprets a given correspondence as the result of any one of several possible combinations, and this leads him to verify his hypotheses by observing their consequences, we know that propositional operations are involved. (Inhelder & Piaget, 1958, p. 279)

   4) In Every Age, Philosophical Thinking Exploits Some Dominant Concepts

   In every age, philosophical thinking exploits some dominant concepts and makes its greatest headway in solving problems conceived in terms of them. The seventeenth- and eighteenth-century philosophers construed knowledge, knower, and known in terms of sense data and their association. Descartes' self-examination gave classical psychology the mind and its contents as a starting point. Locke set up sensory immediacy as the new criterion of the real... Hobbes provided the genetic method of building up complex ideas from simple ones... and, in another quarter, still true to the Hobbesian method, Pavlov built intellect out of conditioned reﬂexes and Loeb built life out of tropisms. (S. Langer, 1962, p. 54)

   5) The Experimental Differentiation of Deductive and Inductive Reasoning

   Experiments on deductive reasoning show that subjects are inﬂuenced sufficiently by their experience for their reasoning to differ from that described by a purely deductive system, whilst experiments on inductive reasoning lead to the view that an understanding of the strategies used by adult subjects in attaining concepts involves reference to higher-order concepts of a logical and deductive nature. (Bolton, 1972, p. 154)

   6) The Power of Machine Thought

   There are now machines in the world that think, that learn and create. Moreover, their ability to do these things is going to increase rapidly until-in the visible future-the range of problems they can handle will be coextensive with the range to which the human mind has been applied. (Newell & Simon, quoted in Weizenbaum, 1976, p. 138)

   7) Thinking Is Sometimes Accompanied by Action and Sometimes Not

   But how does it happen that thinking is sometimes accompanied by action and sometimes not, sometimes by motion, and sometimes not? It looks as if almost the same thing happens as in the case of reasoning and making inferences about unchanging objects. But in that case the end is a speculative proposition... whereas here the conclusion which results from the two premises is an action.... I need covering; a cloak is a covering. I need a cloak. What I need, I have to make; I need a cloak. I have to make a cloak. And the conclusion, the "I have to make a cloak," is an action. (Nussbaum, 1978, p. 40)

   8) The Growth of Philosophy

   It is well to remember that when philosophy emerged in Greece in the sixth century, B.C., it did not burst suddenly out of the Mediterranean blue. The development of societies of reasoning creatures-what we call civilization-had been a process to be measured not in thousands but in millions of years. Human beings became civilized as they became reasonable, and for an animal to begin to reason and to learn how to improve its reasoning is a long, slow process. So thinking had been going on for ages before Greece-slowly improving itself, uncovering the pitfalls to be avoided by forethought, endeavoring to weigh alternative sets of consequences intellectually. What happened in the sixth century, B.C., is that thinking turned round on itself; people began to think about thinking, and the momentous event, the culmination of the long process to that point, was in fact the birth of philosophy. (Lipman, Sharp & Oscanyan, 1980, p. xi)

   9) Thought Is, in Great Part, a Public Activity

   The way to look at thought is not to assume that there is a parallel thread of correlated affects or internal experiences that go with it in some regular way. It's not of course that people don't have internal experiences, of course they do; but that when you ask what is the state of mind of someone, say while he or she is performing a ritual, it's hard to believe that such experiences are the same for all people involved.... The thinking, and indeed the feeling in an odd sort of way, is really going on in public. They are really saying what they're saying, doing what they're doing, meaning what they're meaning. Thought is, in great part anyway, a public activity. (Geertz, quoted in J. Miller, 1983, pp. 202-203)

    10) In Thinking, Everything Needs to Be Made as Simple as Possible

   Everything should be made as simple as possible, but not simpler. (Einstein, quoted in Minsky, 1986, p. 17)

    11) The Conditions for the Construction of Formal Thought

   What, in effect, are the conditions for the construction of formal thought? The child must not only apply operations to objects-in other words, mentally execute possible actions on them-he must also "reﬂect" those operations in the absence of the objects which are replaced by pure propositions. Thus, "reﬂection" is thought raised to the second power. Concrete thinking is the representation of a possible action, and formal thinking is the representation of a representation of possible action.... It is not surprising, therefore, that the system of concrete operations must be completed during the last years of childhood before it can be "reﬂected" by formal operations. In terms of their function, formal operations do not differ from concrete operations except that they are applied to hypotheses or propositions [whose logic is] an abstract translation of the system of "inference" that governs concrete operations. (Piaget, quoted in Minsky, 1986, p. 237)

    12) Language Enables the Rehearsal of Thought and, Thereby, Commitment to Long- Term Memory

   [E]ven a human being today (hence, a fortiori, a remote ancestor of contemporary human beings) cannot easily or ordinarily maintain uninterrupted attention on a single problem for more than a few tens of seconds. Yet we work on problems that require vastly more time. The way we do that (as we can observe by watching ourselves) requires periods of mulling to be followed by periods of recapitulation, describing to ourselves what seems to have gone on during the mulling, leading to whatever intermediate results we have reached. This has an obvious function: namely, by rehearsing these interim results... we commit them to memory, for the immediate contents of the stream of consciousness are very quickly lost unless rehearsed.... Given language, we can describe to ourselves what seemed to occur during the mulling that led to a judgment, produce a rehearsable version of the reaching-a-judgment process, and commit that to long-term memory by in fact rehearsing it. (Margolis, 1987, p. 60)

Historical dictionary of quotations in cognitive science > Thinking

См. также в других словарях:

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