Перевод: с английского на все языки

со всех языков на английский

Со всех языков на:
Английский
С английского на:
Русский

semantic representation

Толкование Перевод

1 semantic representation

Лингвистика: семантическое представление

Универсальный англо-русский словарь > semantic representation
2 logical-semantic representation

Лингвистика: логико-семантическое представление

Универсальный англо-русский словарь > logical-semantic representation
3 semantic

sɪˈmæntɪk прил.;
линг. семантический semantic analysis ≈ семантический анализ semantic representation ≈ семантическое представление семантический (специальное) смысловой axiomatic ~ вчт. аксиоматическая семантика semantic вчт. семантический ~ лингв. семантический

Большой англо-русский и русско-английский словарь > semantic
4 semantic

[sɪ'mæntɪk]
прил.; лингв.

семантический

semantic analysis — семантический анализ

semantic representation — семантическое представление

semantic field — семантическое поле

Англо-русский современный словарь > semantic
5 semantic network

= semantic net

семантическая сеть

в ИИ - способ представления знаний в виде графа, вершины которого представляют собой именованные узлы, обозначающие объекты, факты или понятия, а соединяющие их именованные дуги, именуемые связями, - отношения между объектами. Связи можно использовать для отображения свойств наследования или подчинённости. Выводы по такой сети делаются путём прослеживания связей. Работы по теории семантических сетей развивались в основном для решения задачи понимания естественных языков, требующей намного больше знаний, чем для работы ЭС. Классическая семантическая сеть разделяется на два уровня, конкретную и абстрактную семантические сети. Конкретная семантическая сеть (concrete semantic network) содержит один или несколько разделов знаний (knowledge slice) с детальной информацией по концепциям и их отношениям (ассоциациям). Абстрактная семантическая сеть (abstract semantic network) содержит только доменные концепции (domain concept) и их связи с разделами знаний конкретной семантической сети

см. тж. AI, knowledge representation

Англо-русский толковый словарь терминов и сокращений по ВТ, Интернету и программированию. > semantic network
6 Semantic Web
1. концепция (поколение) Web 3.0
концепция (поколение) Web 3.0
семантический веб
Развитие концепции Web 2.0 в направлении углубления семантических связей и механизмов их обработки.
Иногда термин Web 3.0 заменяется термином Semantic Web и наоборот в зависимости от контекста.
Semantic Web — это слияние Web-технологий и науки о представлении знаний (knowledge representation, KR), являющейся подобластью искусственного интеллекта (artificial intelligence, AI), направленное на создание и поддержание (потенциально сложных) моделей мира, которые позволяют рассуждать о себе и о связанной с ними информации.
В основе Semantic Web лежат языки Resource Description Framework (RDF) и Web Ontology Language (OWL). Консорциум W3C работает над новыми подходами, вроде GRDDL (Gleaning Resource Descriptions from Dialects of Languages) и RDFa (набор расширений RDF), чтобы стандартизировать связи структурированных данных с инструкциями по их преобразованию и включению в существующие Web-ресурсы.
[ http://www.morepc.ru/dict/]

Тематики
- информационные технологии в целом
EN
- Semantic Web
- Web 3.0
Англо-русский словарь нормативно-технической терминологии > Semantic Web
7 semantic interpretation

семантическая интерпретация

в ИИ - процесс выделения смысла из высказывания в форме выражения на некотором языке представлений (representation language) (см также AI, pragmatic interpretation)

Англо-русский толковый словарь терминов и сокращений по ВТ, Интернету и программированию. > semantic interpretation
8 knowledge representation

= KR

представление знаний

организация знаний (фактов, правил) в виде, пригодном для компьютерной обработки. Одна из фундаментальных проблем ИИ. Ранние формы представления знаний - продукционные системы, семантические сети, сценарии, фреймы и объекты. Более современные - агенты и концептуальные графы. Наиболее сложные вопросы в этой области - представление времени, причинности и метазнаний

см. тж. AI, causality, knowledge base, KRL, metaknowledge, semantic network, uncertain knowledge

Англо-русский толковый словарь терминов и сокращений по ВТ, Интернету и программированию. > knowledge representation
9 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
10 Bibliography

■ Aitchison, J. (1987). Noam Chomsky: Consensus and controversy. New York: Falmer Press.

■ Anderson, J. R. (1980). Cognitive psychology and its implications. San Francisco: W. H. Freeman.

■ Anderson, J. R. (1983). The architecture of cognition. Cambridge, MA: Harvard University Press.

■ Anderson, J. R. (1995). Cognitive psychology and its implications (4th ed.). New York: W. H. Freeman.

■ Archilochus (1971). In M. L. West (Ed.), Iambi et elegi graeci (Vol. 1). Oxford: Oxford University Press.

■ Armstrong, D. M. (1990). The causal theory of the mind. In W. G. Lycan (Ed.), Mind and cognition: A reader (pp. 37-47). Cambridge, MA: Basil Blackwell. (Originally published in 1981 in The nature of mind and other essays, Ithaca, NY: University Press).

■ Atkins, P. W. (1992). Creation revisited. Oxford: W. H. Freeman & Company.

■ Austin, J. L. (1962). How to do things with words. Cambridge, MA: Harvard University Press.

■ Bacon, F. (1878). Of the proficience and advancement of learning divine and human. In The works of Francis Bacon (Vol. 1). Cambridge, MA: Hurd & Houghton.

■ Bacon, R. (1928). Opus majus (Vol. 2). R. B. Burke (Trans.). Philadelphia, PA: University of Pennsylvania Press.

■ Bar-Hillel, Y. (1960). The present status of automatic translation of languages. In F. L. Alt (Ed.), Advances in computers (Vol. 1). New York: Academic Press.

■ Barr, A., & E. A. Feigenbaum (Eds.) (1981). The handbook of artificial intelligence (Vol. 1). Reading, MA: Addison-Wesley.

■ Barr, A., & E. A. Feigenbaum (Eds.) (1982). The handbook of artificial intelligence (Vol. 2). Los Altos, CA: William Kaufman.

■ Barron, F. X. (1963). The needs for order and for disorder as motives in creative activity. In C. W. Taylor & F. X. Barron (Eds.), Scientific creativity: Its rec ognition and development (pp. 153-160). New York: Wiley.

■ Bartlett, F. C. (1932). Remembering: A study in experimental and social psychology. Cambridge: Cambridge University Press.

■ Bartley, S. H. (1969). Principles of perception. London: Harper & Row.

■ Barzun, J. (1959). The house of intellect. New York: Harper & Row.

■ Beach, F. A., D. O. Hebb, C. T. Morgan & H. W. Nissen (Eds.) (1960). The neu ropsychology of Lashley. New York: McGraw-Hill.

■ Berkeley, G. (1996). Principles of human knowledge: Three Dialogues. Oxford: Oxford University Press. (Originally published in 1710.)

■ Berlin, I. (1953). The hedgehog and the fox: An essay on Tolstoy's view of history. NY: Simon & Schuster.

■ Bierwisch, J. (1970). Semantics. In J. Lyons (Ed.), New horizons in linguistics. Baltimore: Penguin Books.

■ Black, H. C. (1951). Black's law dictionary. St. Paul, MN: West Publishing.

■ Bloom, A. (1981). The linguistic shaping of thought: A study in the impact of language on thinking in China and the West. Hillsdale, NJ: Erlbaum.

■ Bobrow, D. G., & D. A. Norman (1975). Some principles of memory schemata. In D. G. Bobrow & A. Collins (Eds.), Representation and understanding: Stud ies in Cognitive Science (pp. 131-149). New York: Academic Press.

■ Boden, M. A. (1977). Artificial intelligence and natural man. New York: Basic Books.

■ Boden, M. A. (1981). Minds and mechanisms. Ithaca, NY: Cornell University Press.

■ Boden, M. A. (1990a). The creative mind: Myths and mechanisms. London: Cardinal.

■ Boden, M. A. (1990b). The philosophy of artificial intelligence. Oxford: Oxford University Press.

■ Boden, M. A. (1994). Precis of The creative mind: Myths and mechanisms. Behavioral and brain sciences 17, 519-570.

■ Boden, M. (1996). Creativity. In M. Boden (Ed.), Artificial Intelligence (2nd ed.). San Diego: Academic Press.

■ Bolter, J. D. (1984). Turing's man: Western culture in the computer age. Chapel Hill, NC: University of North Carolina Press.

■ Bolton, N. (1972). The psychology of thinking. London: Methuen.

■ Bourne, L. E. (1973). Some forms of cognition: A critical analysis of several papers. In R. Solso (Ed.), Contemporary issues in cognitive psychology (pp. 313324). Loyola Symposium on Cognitive Psychology (Chicago 1972). Washington, DC: Winston.

■ Bransford, J. D., N. S. McCarrell, J. J. Franks & K. E. Nitsch (1977). Toward unexplaining memory. In R. Shaw & J. D. Bransford (Eds.), Perceiving, acting, and knowing (pp. 431-466). Hillsdale, NJ: Lawrence Erlbaum Associates.

■ Breger, L. (1981). Freud's unfinished journey. London: Routledge & Kegan Paul.

■ Brehmer, B. (1986). In one word: Not from experience. In H. R. Arkes & K. Hammond (Eds.), Judgment and decision making: An interdisciplinary reader (pp. 705-719). Cambridge: Cambridge University Press.

■ Bresnan, J. (1978). A realistic transformational grammar. In M. Halle, J. Bresnan & G. A. Miller (Eds.), Linguistic theory and psychological reality (pp. 1-59). Cambridge, MA: MIT Press.

■ Brislin, R. W., W. J. Lonner & R. M. Thorndike (Eds.) (1973). Cross- cultural research methods. New York: Wiley.

■ Bronowski, J. (1977). A sense of the future: Essays in natural philosophy. P. E. Ariotti with R. Bronowski (Eds.). Cambridge, MA: MIT Press.

■ Bronowski, J. (1978). The origins of knowledge and imagination. New Haven, CT: Yale University Press.

■ Brown, R. O. (1973). A first language: The early stages. Cambridge, MA: Harvard University Press.

■ Brown, T. (1970). Lectures on the philosophy of the human mind. In R. Brown (Ed.), Between Hume and Mill: An anthology of British philosophy- 1749- 1843 (pp. 330-387). New York: Random House/Modern Library.

■ Bruner, J. S., J. Goodnow & G. Austin (1956). A study of thinking. New York: Wiley.

■ Calvin, W. H. (1990). The cerebral symphony: Seashore reﬂections on the structure of consciousness. New York: Bantam.

■ Campbell, J. (1982). Grammatical man: Information, entropy, language, and life. New York: Simon & Schuster.

■ Campbell, J. (1989). The improbable machine. New York: Simon & Schuster.

■ Carlyle, T. (1966). On heroes, hero- worship and the heroic in history. Lincoln: University of Nebraska Press. (Originally published in 1841.)

■ Carnap, R. (1959). The elimination of metaphysics through logical analysis of language [Ueberwindung der Metaphysik durch logische Analyse der Sprache]. In A. J. Ayer (Ed.), Logical positivism (pp. 60-81) A. Pap (Trans). New York: Free Press. (Originally published in 1932.)

■ Cassirer, E. (1946). Language and myth. New York: Harper and Brothers. Reprinted. New York: Dover Publications, 1953.

■ Cattell, R. B., & H. J. Butcher (1970). Creativity and personality. In P. E. Vernon (Ed.), Creativity. Harmondsworth, England: Penguin Books.

■ Caudill, M., & C. Butler (1990). Naturally intelligent systems. Cambridge, MA: MIT Press/Bradford Books.

■ Chandrasekaran, B. (1990). What kind of information processing is intelligence? A perspective on AI paradigms and a proposal. In D. Partridge & R. Wilks (Eds.), The foundations of artificial intelligence: A sourcebook (pp. 14-46). Cambridge: Cambridge University Press.

■ Charniak, E., & McDermott, D. (1985). Introduction to artificial intelligence. Reading, MA: Addison-Wesley.

■ Chase, W. G., & H. A. Simon (1988). The mind's eye in chess. In A. Collins & E. E. Smith (Eds.), Readings in cognitive science: A perspective from psychology and artificial intelligence (pp. 461-493). San Mateo, CA: Kaufmann.

■ Cheney, D. L., & R. M. Seyfarth (1990). How monkeys see the world: Inside the mind of another species. Chicago: University of Chicago Press.

■ Chi, M.T.H., R. Glaser & E. Rees (1982). Expertise in problem solving. In R. J. Sternberg (Ed.), Advances in the psychology of human intelligence (pp. 7-73). Hillsdale, NJ: Lawrence Erlbaum Associates.

■ Chomsky, N. (1957). Syntactic structures. The Hague: Mouton. Janua Linguarum.

■ Chomsky, N. (1964). A transformational approach to syntax. In J. A. Fodor & J. J. Katz (Eds.), The structure of language: Readings in the philosophy of lan guage (pp. 211-245). Englewood Cliffs, NJ: Prentice-Hall.

■ Chomsky, N. (1965). Aspects of the theory of syntax. Cambridge, MA: MIT Press.

■ Chomsky, N. (1972). Language and mind (enlarged ed.). New York: Harcourt Brace Jovanovich.

■ Chomsky, N. (1979). Language and responsibility. New York: Pantheon.

■ Chomsky, N. (1986). Knowledge of language: Its nature, origin and use. New York: Praeger Special Studies.

■ Churchland, P. (1979). Scientific realism and the plasticity of mind. New York: Cambridge University Press.

■ Churchland, P. M. (1989). A neurocomputational perspective: The nature of mind and the structure of science. Cambridge, MA: MIT Press.

■ Churchland, P. S. (1986). Neurophilosophy. Cambridge, MA: MIT Press/Bradford Books.

■ Clark, A. (1996). Philosophical Foundations. In M. A. Boden (Ed.), Artificial in telligence (2nd ed.). San Diego: Academic Press.

■ Clark, H. H., & T. B. Carlson (1981). Context for comprehension. In J. Long & A. Baddeley (Eds.), Attention and performance (Vol. 9, pp. 313-330). Hillsdale, NJ: Lawrence Erlbaum Associates.

■ Clarke, A. C. (1984). Profiles of the future: An inquiry into the limits of the possible. New York: Holt, Rinehart & Winston.

■ Claxton, G. (1980). Cognitive psychology: A suitable case for what sort of treatment? In G. Claxton (Ed.), Cognitive psychology: New directions (pp. 1-25). London: Routledge & Kegan Paul.

■ Code, M. (1985). Order and organism. Albany, NY: State University of New York Press.

■ Collingwood, R. G. (1972). The idea of history. New York: Oxford University Press.

■ Coopersmith, S. (1967). The antecedents of self- esteem. San Francisco: W. H. Freeman.

■ Copland, A. (1952). Music and imagination. London: Oxford University Press.

■ Coren, S. (1994). The intelligence of dogs. New York: Bantam Books.

■ Cottingham, J. (Ed.) (1996). Western philosophy: An anthology. Oxford: Blackwell Publishers.

■ Cox, C. (1926). The early mental traits of three hundred geniuses. Stanford, CA: Stanford University Press.

■ Craik, K.J.W. (1943). The nature of explanation. Cambridge: Cambridge University Press.

■ Cronbach, L. J. (1990). Essentials of psychological testing (5th ed.). New York: HarperCollins.

■ Cronbach, L. J., & R. E. Snow (1977). Aptitudes and instructional methods. New York: Irvington. Paperback edition, 1981.

■ Csikszentmihalyi, M. (1993). The evolving self. New York: Harper Perennial.

■ Culler, J. (1976). Ferdinand de Saussure. New York: Penguin Books.

■ Curtius, E. R. (1973). European literature and the Latin Middle Ages. W. R. Trask (Trans.). Princeton, NJ: Princeton University Press.

■ D'Alembert, J.L.R. (1963). Preliminary discourse to the encyclopedia of Diderot. R. N. Schwab (Trans.). Indianapolis: Bobbs-Merrill.

■ Damasio, A. (1994). Descartes' error: Emotion, reason, and the human brain. New York: Avon.

■ Dampier, W. C. (1966). A history of modern science. Cambridge: Cambridge University Press.

■ Darwin, C. (1911). The life and letters of Charles Darwin (Vol. 1). Francis Darwin (Ed.). New York: Appleton.

■ Davidson, D. (1970) Mental events. In L. Foster & J. W. Swanson (Eds.), Experience and theory (pp. 79-101). Amherst: University of Massachussetts Press.

■ Davies, P. (1995). About time: Einstein's unfinished revolution. New York: Simon & Schuster/Touchstone.

■ Davis, R., & J. J. King (1977). An overview of production systems. In E. Elcock & D. Michie (Eds.), Machine intelligence 8. Chichester, England: Ellis Horwood.

■ Davis, R., & D. B. Lenat (1982). Knowledge- based systems in artificial intelligence. New York: McGraw-Hill.

■ Dawkins, R. (1982). The extended phenotype: The gene as the unit of selection. Oxford: W. H. Freeman.

■ deKleer, J., & J. S. Brown (1983). Assumptions and ambiguities in mechanistic mental models (1983). In D. Gentner & A. L. Stevens (Eds.), Mental modes (pp. 155-190). Hillsdale, NJ: Lawrence Erlbaum Associates.

■ Dennett, D. C. (1978a). Brainstorms: Philosophical essays on mind and psychology. Montgomery, VT: Bradford Books.

■ Dennett, D. C. (1978b). Toward a cognitive theory of consciousness. In D. C. Dennett, Brainstorms: Philosophical Essays on Mind and Psychology. Montgomery, VT: Bradford Books.

■ Dennett, D. C. (1995). Darwin's dangerous idea: Evolution and the meanings of life. New York: Simon & Schuster/Touchstone.

■ Descartes, R. (1897-1910). Traite de l'homme. In Oeuvres de Descartes (Vol. 11, pp. 119-215). Paris: Charles Adam & Paul Tannery. (Originally published in 1634.)

■ Descartes, R. (1950). Discourse on method. L. J. Laﬂeur (Trans.). New York: Liberal Arts Press. (Originally published in 1637.)

■ Descartes, R. (1951). Meditation on first philosophy. L. J. Laﬂeur (Trans.). New York: Liberal Arts Press. (Originally published in 1641.)

■ Descartes, R. (1955). The philosophical works of Descartes. E. S. Haldane and G.R.T. Ross (Trans.). New York: Dover. (Originally published in 1911 by Cambridge University Press.)

■ Descartes, R. (1967). Discourse on method (Pt. V). In E. S. Haldane and G.R.T. Ross (Eds.), The philosophical works of Descartes (Vol. 1, pp. 106-118). Cambridge: Cambridge University Press. (Originally published in 1637.)

■ Descartes, R. (1970a). Discourse on method. In E. S. Haldane & G.R.T. Ross (Eds.), The philosophical works of Descartes (Vol. 1, pp. 181-200). Cambridge: Cambridge University Press. (Originally published in 1637.)

■ Descartes, R. (1970b). Principles of philosophy. In E. S. Haldane & G.R.T. Ross (Eds.), The philosophical works of Descartes (Vol. 1, pp. 178-291). Cambridge: Cambridge University Press. (Originally published in 1644.)

■ Descartes, R. (1984). Meditations on first philosophy. In J. Cottingham, R. Stoothoff & D. Murduch (Trans.), The philosophical works of Descartes (Vol. 2). Cambridge: Cambridge University Press. (Originally published in 1641.)

■ Descartes, R. (1986). Meditations on first philosophy. J. Cottingham (Trans.). Cambridge: Cambridge University Press. (Originally published in 1641 as Med itationes de prima philosophia.)

■ deWulf, M. (1956). An introduction to scholastic philosophy. Mineola, NY: Dover Books.

■ Dixon, N. F. (1981). Preconscious processing. London: Wiley.

■ Doyle, A. C. (1986). The Boscombe Valley mystery. In Sherlock Holmes: The com plete novels and stories (Vol. 1). New York: Bantam.

■ Dreyfus, H., & S. Dreyfus (1986). Mind over machine. New York: Free Press.

■ Dreyfus, H. L. (1972). What computers can't do: The limits of artificial intelligence (revised ed.). New York: Harper & Row.

■ Dreyfus, H. L., & S. E. Dreyfus (1986). Mind over machine: The power of human intuition and expertise in the era of the computer. New York: Free Press.

■ Edelman, G. M. (1992). Bright air, brilliant fire: On the matter of the mind. New York: Basic Books.

■ Ehrenzweig, A. (1967). The hidden order of art. London: Weidenfeld & Nicolson.

■ Einstein, A., & L. Infeld (1938). The evolution of physics. New York: Simon & Schuster.

■ Eisenstein, S. (1947). Film sense. New York: Harcourt, Brace & World.

■ Everdell, W. R. (1997). The first moderns. Chicago: University of Chicago Press.

■ Eysenck, M. W. (1977). Human memory: Theory, research and individual difference. Oxford: Pergamon.

■ Eysenck, M. W. (1982). Attention and arousal: Cognition and performance. Berlin: Springer.

■ Eysenck, M. W. (1984). A handbook of cognitive psychology. Hillsdale, NJ: Lawrence Erlbaum Associates.

■ Fancher, R. E. (1979). Pioneers of psychology. New York: W. W. Norton.

■ Farrell, B. A. (1981). The standing of psychoanalysis. New York: Oxford University Press.

■ Feldman, D. H. (1980). Beyond universals in cognitive development. Norwood, NJ: Ablex.

■ Fetzer, J. H. (1996). Philosophy and cognitive science (2nd ed.). New York: Paragon House.

■ Finke, R. A. (1990). Creative imagery: Discoveries and inventions in visualization. Hillsdale, NJ: Lawrence Erlbaum Associates.

■ Flanagan, O. (1991). The science of the mind. Cambridge MA: MIT Press/Bradford Books.

■ Fodor, J. (1983). The modularity of mind. Cambridge, MA: MIT Press/Bradford Books.

■ Frege, G. (1972). Conceptual notation. T. W. Bynum (Trans.). Oxford: Clarendon Press. (Originally published in 1879.)

■ Frege, G. (1979). Logic. In H. Hermes, F. Kambartel & F. Kaulbach (Eds.), Gottlob Frege: Posthumous writings. Chicago: University of Chicago Press. (Originally published in 1879-1891.)

■ Freud, S. (1959). Creative writers and day-dreaming. In J. Strachey (Ed.), The standard edition of the complete psychological works of Sigmund Freud (Vol. 9, pp. 143-153). London: Hogarth Press.

■ Freud, S. (1966). Project for a scientific psychology. In J. Strachey (Ed.), The stan dard edition of the complete psychological works of Sigmund Freud (Vol. 1, pp. 295-398). London: Hogarth Press. (Originally published in 1950 as Aus den AnfaЁngen der Psychoanalyse, in London by Imago Publishing.)

■ Freud, S. (1976). Lecture 18-Fixation to traumas-the unconscious. In J. Strachey (Ed.), The standard edition of the complete psychological works of Sigmund Freud (Vol. 16, p. 285). London: Hogarth Press.

■ Galileo, G. (1990). Il saggiatore [The assayer]. In S. Drake (Ed.), Discoveries and opinions of Galileo. New York: Anchor Books. (Originally published in 1623.)

■ Gassendi, P. (1970). Letter to Descartes. In "Objections and replies." In E. S. Haldane & G.R.T. Ross (Eds.), The philosophical works of Descartes (Vol. 2, pp. 179-240). Cambridge: Cambridge University Press. (Originally published in 1641.)

■ Gazzaniga, M. S. (1988). Mind matters: How mind and brain interact to create our conscious lives. Boston: Houghton Mifﬂin in association with MIT Press/Bradford Books.

■ Genesereth, M. R., & N. J. Nilsson (1987). Logical foundations of artificial intelligence. Palo Alto, CA: Morgan Kaufmann.

■ Ghiselin, B. (1952). The creative process. New York: Mentor.

■ Ghiselin, B. (1985). The creative process. Berkeley, CA: University of California Press. (Originally published in 1952.)

■ Gilhooly, K. J. (1996). Thinking: Directed, undirected and creative (3rd ed.). London: Academic Press.

■ Glass, A. L., K. J. Holyoak & J. L. Santa (1979). Cognition. Reading, MA: AddisonWesley.

■ Goody, J. (1977). The domestication of the savage mind. Cambridge: Cambridge University Press.

■ Gruber, H. E. (1980). Darwin on man: A psychological study of scientific creativity (2nd ed.). Chicago: University of Chicago Press.

■ Gruber, H. E., & S. Davis (1988). Inching our way up Mount Olympus: The evolving systems approach to creative thinking. In R. J. Sternberg (Ed.), The nature of creativity: Contemporary psychological perspectives. Cambridge: Cambridge University Press.

■ Guthrie, E. R. (1972). The psychology of learning. New York: Harper. (Originally published in 1935.)

■ Habermas, J. (1972). Knowledge and human interests. Boston: Beacon Press.

■ Hadamard, J. (1945). The psychology of invention in the mathematical field. Princeton, NJ: Princeton University Press.

■ Hand, D. J. (1985). Artificial intelligence and psychiatry. Cambridge: Cambridge University Press.

■ Harris, M. (1981). The language myth. London: Duckworth.

■ Haugeland, J. (Ed.) (1981). Mind design: Philosophy, psychology, artificial intelligence. Cambridge, MA: MIT Press/Bradford Books.

■ Haugeland, J. (1981a). The nature and plausibility of cognitivism. In J. Haugeland (Ed.), Mind design: Philosophy, psychology, artificial intelligence (pp. 243-281). Cambridge, MA: MIT Press.

■ Haugeland, J. (1981b). Semantic engines: An introduction to mind design. In J. Haugeland (Ed.), Mind design: Philosophy, psychology, artificial intelligence (pp. 1-34). Cambridge, MA: MIT Press/Bradford Books.

■ Haugeland, J. (1985). Artificial intelligence: The very idea. Cambridge, MA: MIT Press.

■ Hawkes, T. (1977). Structuralism and semiotics. Berkeley: University of California Press.

■ Hebb, D. O. (1949). The organisation of behaviour. New York: Wiley.

■ Hebb, D. O. (1958). A textbook of psychology. Philadelphia: Saunders.

■ Hegel, G.W.F. (1910). The phenomenology of mind. J. B. Baille (Trans.). London: Sonnenschein. (Originally published as Phaenomenologie des Geistes, 1807.)

■ Heisenberg, W. (1958). Physics and philosophy. New York: Harper & Row.

■ Hempel, C. G. (1966). Philosophy of natural science. Englewood Cliffs, NJ: PrenticeHall.

■ Herman, A. (1997). The idea of decline in Western history. New York: Free Press.

■ Herrnstein, R. J., & E. G. Boring (Eds.) (1965). A source book in the history of psy chology. Cambridge, MA: Harvard University Press.

■ Herzmann, E. (1964). Mozart's creative process. In P. H. Lang (Ed.), The creative world of Mozart (pp. 17-30). London: Oldbourne Press.

■ Hilgard, E. R. (1957). Introduction to psychology. London: Methuen.

■ Hobbes, T. (1651). Leviathan. London: Crooke.

■ Hofstadter, D. R. (1979). Goedel, Escher, Bach: An eternal golden braid. New York: Basic Books.

■ Holliday, S. G., & M. J. Chandler (1986). Wisdom: Explorations in adult competence. Basel, Switzerland: Karger.

■ Horn, J. L. (1986). In R. J. Sternberg (Ed.), Advances in the psychology of human intelligence (Vol. 3). Hillsdale, NJ: Erlbaum.

■ Hull, C. (1943). Principles of behavior. New York: Appleton-Century-Crofts.

■ Hume, D. (1955). An inquiry concerning human understanding. New York: Liberal Arts Press. (Originally published in 1748.)

■ Hume, D. (1975). An enquiry concerning human understanding. In L. A. SelbyBigge (Ed.), Hume's enquiries (3rd. ed., revised P. H. Nidditch). Oxford: Clarendon. (Spelling and punctuation revised.) (Originally published in 1748.)

■ Hume, D. (1978). A treatise of human nature. L. A. Selby-Bigge (Ed.), Hume's enquiries (3rd. ed., revised P. H. Nidditch). Oxford: Clarendon. (With some modifications of spelling and punctuation.) (Originally published in 1690.)

■ Hunt, E. (1973). The memory we must have. In R. C. Schank & K. M. Colby (Eds.), Computer models of thought and language. (pp. 343-371) San Francisco: W. H. Freeman.

■ Husserl, E. (1960). Cartesian meditations. The Hague: Martinus Nijhoff.

■ Inhelder, B., & J. Piaget (1958). The growth of logical thinking from childhood to adolescence. New York: Basic Books. (Originally published in 1955 as De la logique de l'enfant a` la logique de l'adolescent. [Paris: Presses Universitaire de France])

■ James, W. (1890a). The principles of psychology (Vol. 1). New York: Dover Books.

■ James, W. (1890b). The principles of psychology. New York: Henry Holt.

■ Jevons, W. S. (1900). The principles of science (2nd ed.). London: Macmillan.

■ Johnson, G. (1986). Machinery of the mind: Inside the new science of artificial intelli gence. New York: Random House.

■ Johnson, M. L. (1988). Mind, language, machine. New York: St. Martin's Press.

■ Johnson-Laird, P. N. (1983). Mental models: Toward a cognitive science of language, inference, and consciousness. Cambridge, MA: Harvard University Press.

■ Johnson-Laird, P. N. (1988). The computer and the mind: An introduction to cognitive science. Cambridge, MA: Harvard University Press.

■ Jones, E. (1961). The life and work of Sigmund Freud. L. Trilling & S. Marcus (Eds.). London: Hogarth.

■ Jones, R. V. (1985). Complementarity as a way of life. In A. P. French & P. J. Kennedy (Eds.), Niels Bohr: A centenary volume. Cambridge, MA: Harvard University Press.

■ Kant, I. (1933). Critique of Pure Reason (2nd ed.). N. K. Smith (Trans.). London: Macmillan. (Originally published in 1781 as Kritik der reinen Vernunft.)

■ Kant, I. (1891). Solution of the general problems of the Prolegomena. In E. Belfort (Trans.), Kant's Prolegomena. London: Bell. (With minor modifications.) (Originally published in 1783.)

■ Katona, G. (1940). Organizing and memorizing: Studies in the psychology of learning and teaching. New York: Columbia University Press.

■ Kaufman, A. S. (1979). Intelligent testing with the WISC-R. New York: Wiley.

■ Koestler, A. (1964). The act of creation. New York: Arkana (Penguin).

■ Kohlberg, L. (1971). From is to ought. In T. Mischel (Ed.), Cognitive development and epistemology. (pp. 151-235) New York: Academic Press.

■ KoЁhler, W. (1925). The mentality of apes. New York: Liveright.

■ KoЁhler, W. (1927). The mentality of apes (2nd ed.). Ella Winter (Trans.). London: Routledge & Kegan Paul.

■ KoЁhler, W. (1930). Gestalt psychology. London: G. Bell.

■ KoЁhler, W. (1947). Gestalt psychology. New York: Liveright.

■ KoЁhler, W. (1969). The task of Gestalt psychology. Princeton, NJ: Princeton University Press.

■ Kuhn, T. (1970). The structure of scientific revolutions (2nd ed.). Chicago: University of Chicago Press.

■ Langer, E. J. (1989). Mindfulness. Reading, MA: Addison-Wesley.

■ Langer, S. (1962). Philosophical sketches. Baltimore: Johns Hopkins University Press.

■ Langley, P., H. A. Simon, G. L. Bradshaw & J. M. Zytkow (1987). Scientific dis covery: Computational explorations of the creative process. Cambridge, MA: MIT Press.

■ Lashley, K. S. (1951). The problem of serial order in behavior. In L. A. Jeffress (Ed.), Cerebral mechanisms in behavior, the Hixon Symposium (pp. 112-146) New York: Wiley.

■ LeDoux, J. E., & W. Hirst (1986). Mind and brain: Dialogues in cognitive neuroscience. Cambridge: Cambridge University Press.

■ Lehnert, W. (1978). The process of question answering. Hillsdale, NJ: Lawrence Erlbaum Associates.

■ Leiber, J. (1991). Invitation to cognitive science. Oxford: Blackwell.

■ Lenat, D. B., & G. Harris (1978). Designing a rule system that searches for scientific discoveries. In D. A. Waterman & F. Hayes-Roth (Eds.), Pattern directed inference systems (pp. 25-52) New York: Academic Press.

■ Levenson, T. (1995). Measure for measure: A musical history of science. New York: Touchstone. (Originally published in 1994.)

■ Leґvi-Strauss, C. (1963). Structural anthropology. C. Jacobson & B. Grundfest Schoepf (Trans.). New York: Basic Books. (Originally published in 1958.)

■ Levine, M. W., & J. M. Schefner (1981). Fundamentals of sensation and perception. London: Addison-Wesley.

■ Lewis, C. I. (1946). An analysis of knowledge and valuation. LaSalle, IL: Open Court.

■ Lighthill, J. (1972). A report on artificial intelligence. Unpublished manuscript, Science Research Council.

■ Lipman, M., A. M. Sharp & F. S. Oscanyan (1980). Philosophy in the classroom. Philadelphia: Temple University Press.

■ Lippmann, W. (1965). Public opinion. New York: Free Press. (Originally published in 1922.)

■ Locke, J. (1956). An essay concerning human understanding. Chicago: Henry Regnery Co. (Originally published in 1690.)

■ Locke, J. (1975). An essay concerning human understanding. P. H. Nidditch (Ed.). Oxford: Clarendon. (Originally published in 1690.) (With spelling and punctuation modernized and some minor modifications of phrasing.)

■ Lopate, P. (1994). The art of the personal essay. New York: Doubleday/Anchor Books.

■ Lorimer, F. (1929). The growth of reason. London: Kegan Paul. Machlup, F., & U. Mansfield (Eds.) (1983). The study of information. New York: Wiley.

■ Manguel, A. (1996). A history of reading. New York: Viking.

■ Margolis, H. (1987). Patterns, thinking, and cognition. Chicago: University of Chicago Press.

■ Markey, J. F. (1928). The symbolic process. London: Kegan Paul.

■ Martin, R. M. (1969). On Ziff's "Natural and formal languages." In S. Hook (Ed.), Language and philosophy: A symposium (pp. 249-263). New York: New York University Press.

■ Mazlish, B. (1993). The fourth discontinuity: the co- evolution of humans and machines. New Haven, CT: Yale University Press.

■ McCarthy, J., & P. J. Hayes (1969). Some philosophical problems from the standpoint of artificial intelligence. In B. Meltzer & D. Michie (Eds.), Machine intelligence 4. Edinburgh: Edinburgh University Press.

■ McClelland, J. L., D. E. Rumelhart & G. E. Hinton (1986). The appeal of parallel distributed processing. In D. E. Rumelhart, J. L. McClelland & the PDP Research Group (Eds.), Parallel distributed processing: Explorations in the mi crostructure of cognition (Vol. 1, pp. 3-40). Cambridge, MA: MIT Press/ Bradford Books.

■ McCorduck, P. (1979). Machines who think. San Francisco: W. H. Freeman.

■ McLaughlin, T. (1970). Music and communication. London: Faber & Faber.

■ Mednick, S. A. (1962). The associative basis of the creative process. Psychological Review 69, 431-436.

■ Meehl, P. E., & C. J. Golden (1982). Taxometric methods. In Kendall, P. C., & Butcher, J. N. (Eds.), Handbook of research methods in clinical psychology (pp. 127-182). New York: Wiley.

■ Mehler, J., E.C.T. Walker & M. Garrett (Eds.) (1982). Perspectives on mental rep resentation: Experimental and theoretical studies of cognitive processes and ca pacities. Hillsdale, NJ: Lawrence Erlbaum Associates.

■ Mill, J. S. (1900). A system of logic, ratiocinative and inductive: Being a connected view of the principles of evidence and the methods of scientific investigation. London: Longmans, Green.

■ Miller, G. A. (1979, June). A very personal history. Talk to the Cognitive Science Workshop, Cambridge, MA.

■ Miller, J. (1983). States of mind. New York: Pantheon Books.

■ Minsky, M. (1975). A framework for representing knowledge. In P. H. Winston (Ed.), The psychology of computer vision (pp. 211-277). New York: McGrawHill.

■ Minsky, M., & S. Papert (1973). Artificial intelligence. Condon Lectures, Oregon State System of Higher Education, Eugene, Oregon.

■ Minsky, M. L. (1986). The society of mind. New York: Simon & Schuster.

■ Mischel, T. (1976). Psychological explanations and their vicissitudes. In J. K. Cole & W. J. Arnold (Eds.), Nebraska Symposium on motivation (Vol. 23). Lincoln, NB: University of Nebraska Press.

■ Morford, M.P.O., & R. J. Lenardon (1995). Classical mythology (5th ed.). New York: Longman.

■ Murdoch, I. (1954). Under the net. New York: Penguin.

■ Nagel, E. (1959). Methodological issues in psychoanalytic theory. In S. Hook (Ed.), Psychoanalysis, scientific method, and philosophy: A symposium. New York: New York University Press.

■ Nagel, T. (1979). Mortal questions. London: Cambridge University Press.

■ Nagel, T. (1986). The view from nowhere. Oxford: Oxford University Press.

■ Neisser, U. (1967). Cognitive psychology. New York: Appleton-Century-Crofts.

■ Neisser, U. (1972). Changing conceptions of imagery. In P. W. Sheehan (Ed.), The function and nature of imagery (pp. 233-251). London: Academic Press.

■ Neisser, U. (1976). Cognition and reality. San Francisco: W. H. Freeman.

■ Neisser, U. (1978). Memory: What are the important questions? In M. M. Gruneberg, P. E. Morris & R. N. Sykes (Eds.), Practical aspects of memory (pp. 3-24). London: Academic Press.

■ Neisser, U. (1979). The concept of intelligence. In R. J. Sternberg & D. K. Detterman (Eds.), Human intelligence: Perspectives on its theory and measurement (pp. 179-190). Norwood, NJ: Ablex.

■ Nersessian, N. (1992). How do scientists think? Capturing the dynamics of conceptual change in science. In R. N. Giere (Ed.), Cognitive models of science (pp. 3-44). Minneapolis: University of Minnesota Press.

■ Newell, A. (1973a). Artificial intelligence and the concept of mind. In R. C. Schank & K. M. Colby (Eds.), Computer models of thought and language (pp. 1-60). San Francisco: W. H. Freeman.

■ Newell, A. (1973b). You can't play 20 questions with nature and win. In W. G. Chase (Ed.), Visual information processing (pp. 283-310). New York: Academic Press.

■ Newell, A., & H. A. Simon (1963). GPS: A program that simulates human thought. In E. A. Feigenbaum & J. Feldman (Eds.), Computers and thought (pp. 279-293). New York & McGraw-Hill.

■ Newell, A., & H. A. Simon (1972). Human problem solving. Englewood Cliffs, NJ: Prentice-Hall.

■ Nietzsche, F. (1966). Beyond good and evil. W. Kaufmann (Trans.). New York: Vintage. (Originally published in 1885.)

■ Nilsson, N. J. (1971). Problem- solving methods in artificial intelligence. New York: McGraw-Hill.

■ Nussbaum, M. C. (1978). Aristotle's Princeton University Press. De Motu Anamalium. Princeton, NJ:

■ Oersted, H. C. (1920). Thermo-electricity. In Kirstine Meyer (Ed.), H. C. Oersted, Natuurvidenskabelige Skrifter (Vol. 2). Copenhagen: n.p. (Originally published in 1830 in The Edinburgh encyclopaedia.)

■ Ong, W. J. (1982). Orality and literacy: The technologizing of the word. London: Methuen.

■ Onians, R. B. (1954). The origins of European thought. Cambridge, MA: Cambridge University Press.

■ Osgood, C. E. (1960). Method and theory in experimental psychology. New York: Oxford University Press. (Originally published in 1953.)

■ Osgood, C. E. (1966). Language universals and psycholinguistics. In J. H. Greenberg (Ed.), Universals of language (2nd ed., pp. 299-322). Cambridge, MA: MIT Press.

■ Palmer, R. E. (1969). Hermeneutics. Evanston, IL: Northwestern University Press.

■ Peirce, C. S. (1934). Some consequences of four incapacities-Man, a sign. In C. Hartsborne & P. Weiss (Eds.), Collected papers of Charles Saunders Peirce (Vol. 5, pp. 185-189). Cambridge, MA: Harvard University Press.

■ Penfield, W. (1959). In W. Penfield & L. Roberts, Speech and brain mechanisms. Princeton, NJ: Princeton University Press.

■ Penrose, R. (1994). Shadows of the mind: A search for the missing science of conscious ness. Oxford: Oxford University Press.

■ Perkins, D. N. (1981). The mind's best work. Cambridge, MA: Harvard University Press.

■ Peterfreund, E. (1986). The heuristic approach to psychoanalytic therapy. In

■ J. Reppen (Ed.), Analysts at work, (pp. 127-144). Hillsdale, NJ: Analytic Press.

■ Piaget, J. (1952). The origin of intelligence in children. New York: International Universities Press. (Originally published in 1936.)

■ Piaget, J. (1954). Le langage et les opeґrations intellectuelles. Proble` mes de psycho linguistique. Symposium de l'Association de Psychologie Scientifique de Langue Francёaise. Paris: Presses Universitaires de France.

■ Piaget, J. (1977). Problems of equilibration. In H. E. Gruber & J. J. Voneche (Eds.), The essential Piaget (pp. 838-841). London: Routlege & Kegan Paul. (Originally published in 1975 as L'eґquilibration des structures cognitives [Paris: Presses Universitaires de France].)

■ Piaget, J., & B. Inhelder. (1973). Memory and intelligence. New York: Basic Books.

■ Pinker, S. (1994). The language instinct. New York: Morrow.

■ Pinker, S. (1996). Facts about human language relevant to its evolution. In J.-P. Changeux & J. Chavaillon (Eds.), Origins of the human brain. A symposium of the Fyssen foundation (pp. 262-283). Oxford: Clarendon Press. Planck, M. (1949). Scientific autobiography and other papers. F. Gaynor (Trans.). New York: Philosophical Library.

■ Planck, M. (1990). Wissenschaftliche Selbstbiographie. W. Berg (Ed.). Halle, Germany: Deutsche Akademie der Naturforscher Leopoldina.

■ Plato (1892). Meno. In The Dialogues of Plato (B. Jowett, Trans.; Vol. 2). New York: Clarendon. (Originally published circa 380 B.C.)

■ Poincareґ, H. (1913). Mathematical creation. In The foundations of science. G. B. Halsted (Trans.). New York: Science Press.

■ Poincareґ, H. (1921). The foundations of science: Science and hypothesis, the value of science, science and method. G. B. Halstead (Trans.). New York: Science Press.

■ Poincareґ, H. (1929). The foundations of science: Science and hypothesis, the value of science, science and method. New York: Science Press.

■ Poincareґ, H. (1952). Science and method. F. Maitland (Trans.) New York: Dover.

■ Polya, G. (1945). How to solve it. Princeton, NJ: Princeton University Press.

■ Polanyi, M. (1958). Personal knowledge. London: Routledge & Kegan Paul.

■ Popper, K. (1968). Conjectures and refutations: The growth of scientific knowledge. New York: Harper & Row/Basic Books.

■ Popper, K., & J. Eccles (1977). The self and its brain. New York: Springer-Verlag.

■ Popper, K. R. (1959). The logic of scientific discovery. London: Hutchinson.

■ Putnam, H. (1975). Mind, language and reality: Philosophical papers (Vol. 2). Cambridge: Cambridge University Press.

■ Putnam, H. (1987). The faces of realism. LaSalle, IL: Open Court.

■ Pylyshyn, Z. W. (1981). The imagery debate: Analog media versus tacit knowledge. In N. Block (Ed.), Imagery (pp. 151-206). Cambridge, MA: MIT Press.

■ Pylyshyn, Z. W. (1984). Computation and cognition: Towards a foundation for cog nitive science. Cambridge, MA: MIT Press/Bradford Books.

■ Quillian, M. R. (1968). Semantic memory. In M. Minsky (Ed.), Semantic information processing (pp. 216-260). Cambridge, MA: MIT Press.

■ Quine, W.V.O. (1960). Word and object. Cambridge, MA: Harvard University Press.

■ Rabbitt, P.M.A., & S. Dornic (Eds.). Attention and performance (Vol. 5). London: Academic Press.

■ Rawlins, G.J.E. (1997). Slaves of the Machine: The quickening of computer technology. Cambridge, MA: MIT Press/Bradford Books.

■ Reid, T. (1970). An inquiry into the human mind on the principles of common sense. In R. Brown (Ed.), Between Hume and Mill: An anthology of British philosophy- 1749- 1843 (pp. 151-178). New York: Random House/Modern Library.

■ Reitman, W. (1970). What does it take to remember? In D. A. Norman (Ed.), Models of human memory (pp. 470-510). London: Academic Press.

■ Ricoeur, P. (1974). Structure and hermeneutics. In D. I. Ihde (Ed.), The conﬂict of interpretations: Essays in hermeneutics (pp. 27-61). Evanston, IL: Northwestern University Press.

■ Robinson, D. N. (1986). An intellectual history of psychology. Madison: University of Wisconsin Press.

■ Rorty, R. (1979). Philosophy and the mirror of nature. Princeton, NJ: Princeton University Press.

■ Rosch, E. (1977). Human categorization. In N. Warren (Ed.), Studies in cross cultural psychology (Vol. 1, pp. 1-49) London: Academic Press.

■ Rosch, E. (1978). Principles of categorization. In E. Rosch & B. B. Lloyd (Eds.), Cognition and categorization (pp. 27-48). Hillsdale, NJ: Lawrence Erlbaum Associates.

■ Rosch, E., & B. B. Lloyd (1978). Principles of categorization. In E. Rosch & B. B. Lloyd (Eds.), Cognition and categorization. Hillsdale, NJ: Lawrence Erlbaum Associates.

■ Rose, S. (1970). The chemistry of life. Baltimore: Penguin Books.

■ Rose, S. (1976). The conscious brain (updated ed.). New York: Random House.

■ Rose, S. (1993). The making of memory: From molecules to mind. New York: Anchor Books. (Originally published in 1992)

■ Roszak, T. (1994). The cult of information: A neo- Luddite treatise on high- tech, artificial intelligence, and the true art of thinking (2nd ed.). Berkeley: University of California Press.

■ Royce, J. R., & W. W. Rozeboom (Eds.) (1972). The psychology of knowing. New York: Gordon & Breach.

■ Rumelhart, D. E. (1977). Introduction to human information processing. New York: Wiley.

■ Rumelhart, D. E. (1980). Schemata: The building blocks of cognition. In R. J. Spiro, B. Bruce & W. F. Brewer (Eds.), Theoretical issues in reading comprehension. Hillsdale, NJ: Lawrence Erlbaum Associates.

■ Rumelhart, D. E., & J. L. McClelland (1986). On learning the past tenses of English verbs. In J. L. McClelland & D. E. Rumelhart (Eds.), Parallel distributed processing: Explorations in the microstructure of cognition (Vol. 2). Cambridge, MA: MIT Press.

■ Rumelhart, D. E., P. Smolensky, J. L. McClelland & G. E. Hinton (1986). Schemata and sequential thought processes in PDP models. In J. L. McClelland, D. E. Rumelhart & the PDP Research Group (Eds.), Parallel Distributed Processing (Vol. 2, pp. 7-57). Cambridge, MA: MIT Press.

■ Russell, B. (1927). An outline of philosophy. London: G. Allen & Unwin.

■ Russell, B. (1961). History of Western philosophy. London: George Allen & Unwin.

■ Russell, B. (1965). How I write. In Portraits from memory and other essays. London: Allen & Unwin.

■ Russell, B. (1992). In N. Griffin (Ed.), The selected letters of Bertrand Russell (Vol. 1), The private years, 1884- 1914. Boston: Houghton Mifﬂin. Ryecroft, C. (1966). Psychoanalysis observed. London: Constable.

■ Sagan, C. (1978). The dragons of Eden: Speculations on the evolution of human intel ligence. New York: Ballantine Books.

■ Salthouse, T. A. (1992). Expertise as the circumvention of human processing limitations. In K. A. Ericsson & J. Smith (Eds.), Toward a general theory of expertise: Prospects and limits (pp. 172-194). Cambridge: Cambridge University Press.

■ Sanford, A. J. (1987). The mind of man: Models of human understanding. New Haven, CT: Yale University Press.

■ Sapir, E. (1921). Language. New York: Harcourt, Brace, and World.

■ Sapir, E. (1964). Culture, language, and personality. Berkeley: University of California Press. (Originally published in 1941.)

■ Sapir, E. (1985). The status of linguistics as a science. In D. G. Mandelbaum (Ed.), Selected writings of Edward Sapir in language, culture and personality (pp. 160166). Berkeley: University of California Press. (Originally published in 1929).

■ Scardmalia, M., & C. Bereiter (1992). Literate expertise. In K. A. Ericsson & J. Smith (Eds.), Toward a general theory of expertise: Prospects and limits (pp. 172-194). Cambridge: Cambridge University Press.

■ Schafer, R. (1954). Psychoanalytic interpretation in Rorschach testing. New York: Grune & Stratten.

■ Schank, R. C. (1973). Identification of conceptualizations underlying natural language. In R. C. Schank & K. M. Colby (Eds.), Computer models of thought and language (pp. 187-248). San Francisco: W. H. Freeman.

■ Schank, R. C. (1976). The role of memory in language processing. In C. N. Cofer (Ed.), The structure of human memory. (pp. 162-189) San Francisco: W. H. Freeman.

■ Schank, R. C. (1986). Explanation patterns: Understanding mechanically and creatively. Hillsdale, NJ: Lawrence Erlbaum Associates.

■ Schank, R. C., & R. P. Abelson (1977). Scripts, plans, goals, and understanding. Hillsdale, NJ: Lawrence Erlbaum Associates.

■ SchroЁdinger, E. (1951). Science and humanism. Cambridge: Cambridge University Press.

■ Searle, J. R. (1981a). Minds, brains, and programs. In J. Haugeland (Ed.), Mind design: Philosophy, psychology, artificial intelligence (pp. 282-306). Cambridge, MA: MIT Press.

■ Searle, J. R. (1981b). Minds, brains and programs. In D. Hofstadter & D. Dennett (Eds.), The mind's I (pp. 353-373). New York: Basic Books.

■ Searle, J. R. (1983). Intentionality. New York: Cambridge University Press.

■ Serres, M. (1982). The origin of language: Biology, information theory, and thermodynamics. M. Anderson (Trans.). In J. V. Harari & D. F. Bell (Eds.), Hermes: Literature, science, philosophy (pp. 71-83). Baltimore: Johns Hopkins University Press.

■ Simon, H. A. (1966). Scientific discovery and the psychology of problem solving. In R. G. Colodny (Ed.), Mind and cosmos: Essays in contemporary science and philosophy (pp. 22-40). Pittsburgh: University of Pittsburgh Press.

■ Simon, H. A. (1979). Models of thought. New Haven, CT: Yale University Press.

■ Simon, H. A. (1989). The scientist as a problem solver. In D. Klahr & K. Kotovsky (Eds.), Complex information processing: The impact of Herbert Simon. Hillsdale, N.J.: Lawrence Erlbaum Associates.

■ Simon, H. A., & C. Kaplan (1989). Foundations of cognitive science. In M. Posner (Ed.), Foundations of cognitive science (pp. 1-47). Cambridge, MA: MIT Press.

■ Simonton, D. K. (1988). Creativity, leadership and chance. In R. J. Sternberg (Ed.), The nature of creativity. Cambridge: Cambridge University Press.

■ Skinner, B. F. (1974). About behaviorism. New York: Knopf.

■ Smith, E. E. (1988). Concepts and thought. In J. Sternberg & E. E. Smith (Eds.), The psychology of human thought (pp. 19-49). Cambridge: Cambridge University Press.

■ Smith, E. E. (1990). Thinking: Introduction. In D. N. Osherson & E. E. Smith (Eds.), Thinking. An invitation to cognitive science. (Vol. 3, pp. 1-2). Cambridge, MA: MIT Press.

■ Socrates. (1958). Meno. In E. H. Warmington & P. O. Rouse (Eds.), Great dialogues of Plato W.H.D. Rouse (Trans.). New York: New American Library. (Original publication date unknown.)

■ Solso, R. L. (1974). Theories of retrieval. In R. L. Solso (Ed.), Theories in cognitive psychology. Potomac, MD: Lawrence Erlbaum Associates.

■ Spencer, H. (1896). The principles of psychology. New York: Appleton-CenturyCrofts.

■ Steiner, G. (1975). After Babel: Aspects of language and translation. New York: Oxford University Press.

■ Sternberg, R. J. (1977). Intelligence, information processing, and analogical reasoning. Hillsdale, NJ: Lawrence Erlbaum Associates.

■ Sternberg, R. J. (1994). Intelligence. In R. J. Sternberg, Thinking and problem solving. San Diego: Academic Press.

■ Sternberg, R. J., & J. E. Davidson (1985). Cognitive development in gifted and talented. In F. D. Horowitz & M. O'Brien (Eds.), The gifted and talented (pp. 103-135). Washington, DC: American Psychological Association.

■ Storr, A. (1993). The dynamics of creation. New York: Ballantine Books. (Originally published in 1972.)

■ Stumpf, S. E. (1994). Philosophy: History and problems (5th ed.). New York: McGraw-Hill.

■ Sulloway, F. J. (1996). Born to rebel: Birth order, family dynamics, and creative lives. New York: Random House/Vintage Books.

■ Thorndike, E. L. (1906). Principles of teaching. New York: A. G. Seiler.

■ Thorndike, E. L. (1970). Animal intelligence: Experimental studies. Darien, CT: Hafner Publishing Co. (Originally published in 1911.)

■ Titchener, E. B. (1910). A textbook of psychology. New York: Macmillan.

■ Titchener, E. B. (1914). A primer of psychology. New York: Macmillan.

■ Toulmin, S. (1957). The philosophy of science. London: Hutchinson.

■ Tulving, E. (1972). Episodic and semantic memory. In E. Tulving & W. Donaldson (Eds.), Organisation of memory. London: Academic Press.

■ Turing, A. (1946). In B. E. Carpenter & R. W. Doran (Eds.), ACE reports of 1946 and other papers. Cambridge, MA: MIT Press.

■ Turkle, S. (1984). Computers and the second self: Computers and the human spirit. New York: Simon & Schuster.

■ Tyler, S. A. (1978). The said and the unsaid: Mind, meaning, and culture. New York: Academic Press.

■ van Heijenoort (Ed.) (1967). From Frege to Goedel. Cambridge: Harvard University Press.

■ Varela, F. J. (1984). The creative circle: Sketches on the natural history of circularity. In P. Watzlawick (Ed.), The invented reality (pp. 309-324). New York: W. W. Norton.

■ Voltaire (1961). On the Penseґs of M. Pascal. In Philosophical letters (pp. 119-146). E. Dilworth (Trans.). Indianapolis: Bobbs-Merrill.

■ Wagman, M. (1991a). Artificial intelligence and human cognition: A theoretical inter comparison of two realms of intellect. Westport, CT: Praeger.

■ Wagman, M. (1991b). Cognitive science and concepts of mind: Toward a general theory of human and artificial intelligence. Westport, CT: Praeger.

■ Wagman, M. (1993). Cognitive psychology and artificial intelligence: Theory and re search in cognitive science. Westport, CT: Praeger.

■ Wagman, M. (1995). The sciences of cognition: Theory and research in psychology and artificial intelligence. Westport, CT: Praeger.

■ Wagman, M. (1996). Human intellect and cognitive science: Toward a general unified theory of intelligence. Westport, CT: Praeger.

■ Wagman, M. (1997a). Cognitive science and the symbolic operations of human and artificial intelligence: Theory and research into the intellective processes. Westport, CT: Praeger.

■ Wagman, M. (1997b). The general unified theory of intelligence: Central conceptions and specific application to domains of cognitive science. Westport, CT: Praeger.

■ Wagman, M. (1998a). Cognitive science and the mind- body problem: From philosophy to psychology to artificial intelligence to imaging of the brain. Westport, CT: Praeger.

■ Wagman, M. (1998b). Language and thought in humans and computers: Theory and research in psychology, artificial intelligence, and neural science. Westport, CT: Praeger.

■ Wagman, M. (1998c). The ultimate objectives of artificial intelligence: Theoretical and research foundations, philosophical and psychological implications. Westport, CT: Praeger.

■ Wagman, M. (1999). The human mind according to artificial intelligence: Theory, re search, and implications. Westport, CT: Praeger.

■ Wagman, M. (2000). Scientific discovery processes in humans and computers: Theory and research in psychology and artificial intelligence. Westport, CT: Praeger.

■ Wall, R. (1972). Introduction to mathematical linguistics. Englewood Cliffs, NJ: Prentice-Hall.

■ Wallas, G. (1926). The Art of Thought. New York: Harcourt, Brace & Co.

■ Wason, P. (1977). Self contradictions. In P. Johnson-Laird & P. Wason (Eds.), Thinking: Readings in cognitive science. Cambridge: Cambridge University Press.

■ Wason, P. C., & P. N. Johnson-Laird. (1972). Psychology of reasoning: Structure and content. Cambridge, MA: Harvard University Press.

■ Watson, J. (1930). Behaviorism. New York: W. W. Norton.

■ Watzlawick, P. (1984). Epilogue. In P. Watzlawick (Ed.), The invented reality. New York: W. W. Norton, 1984.

■ Weinberg, S. (1977). The first three minutes: A modern view of the origin of the uni verse. New York: Basic Books.

■ Weisberg, R. W. (1986). Creativity: Genius and other myths. New York: W. H. Freeman.

■ Weizenbaum, J. (1976). Computer power and human reason: From judgment to cal culation. San Francisco: W. H. Freeman.

■ Wertheimer, M. (1945). Productive thinking. New York: Harper & Bros.

■ Whitehead, A. N. (1925). Science and the modern world. New York: Macmillan.

■ Whorf, B. L. (1956). In J. B. Carroll (Ed.), Language, thought and reality: Selected writings of Benjamin Lee Whorf. Cambridge, MA: MIT Press.

■ Whyte, L. L. (1962). The unconscious before Freud. New York: Anchor Books.

■ Wiener, N. (1954). The human use of human beings. Boston: Houghton Mifﬂin.

■ Wiener, N. (1964). God & Golem, Inc.: A comment on certain points where cybernetics impinges on religion. Cambridge, MA: MIT Press.

■ Winograd, T. (1972). Understanding natural language. New York: Academic Press.

■ Winston, P. H. (1987). Artificial intelligence: A perspective. In E. L. Grimson & R. S. Patil (Eds.), AI in the 1980s and beyond (pp. 1-12). Cambridge, MA: MIT Press.

■ Winston, P. H. (Ed.) (1975). The psychology of computer vision. New York: McGrawHill.

■ Wittgenstein, L. (1953). Philosophical investigations. Oxford: Basil Blackwell.

■ Wittgenstein, L. (1958). The blue and brown books. New York: Harper Colophon.

■ Woods, W. A. (1975). What's in a link: Foundations for semantic networks. In D. G. Bobrow & A. Collins (Eds.), Representations and understanding: Studies in cognitive science (pp. 35-84). New York: Academic Press.

■ Woodworth, R. S. (1938). Experimental psychology. New York: Holt; London: Methuen (1939).

■ Wundt, W. (1904). Principles of physiological psychology (Vol. 1). E. B. Titchener (Trans.). New York: Macmillan.

■ Wundt, W. (1907). Lectures on human and animal psychology. J. E. Creighton & E. B. Titchener (Trans.). New York: Macmillan.

■ Young, J. Z. (1978). Programs of the brain. New York: Oxford University Press.

■ Ziman, J. (1978). Reliable knowledge: An exploration of the grounds for belief in science. Cambridge: Cambridge University Press.

Historical dictionary of quotations in cognitive science > Bibliography
11 Web 3.0
1. концепция (поколение) Web 3.0
концепция (поколение) Web 3.0
семантический веб
Развитие концепции Web 2.0 в направлении углубления семантических связей и механизмов их обработки.
Иногда термин Web 3.0 заменяется термином Semantic Web и наоборот в зависимости от контекста.
Semantic Web — это слияние Web-технологий и науки о представлении знаний (knowledge representation, KR), являющейся подобластью искусственного интеллекта (artificial intelligence, AI), направленное на создание и поддержание (потенциально сложных) моделей мира, которые позволяют рассуждать о себе и о связанной с ними информации.
В основе Semantic Web лежат языки Resource Description Framework (RDF) и Web Ontology Language (OWL). Консорциум W3C работает над новыми подходами, вроде GRDDL (Gleaning Resource Descriptions from Dialects of Languages) и RDFa (набор расширений RDF), чтобы стандартизировать связи структурированных данных с инструкциями по их преобразованию и включению в существующие Web-ресурсы.
[ http://www.morepc.ru/dict/]

Тематики
- информационные технологии в целом
EN
- Semantic Web
- Web 3.0
Англо-русский словарь нормативно-технической терминологии > Web 3.0
12 Grammar

   1) Grammar as Analogous to a Scientific Theory

   I think that the failure to offer a precise account of the notion "grammar" is not just a superficial defect in linguistic theory that can be remedied by adding one more definition. It seems to me that until this notion is clarified, no part of linguistic theory can achieve anything like a satisfactory development.... I have been discussing a grammar of a particular language here as analogous to a particular scientific theory, dealing with its subject matter (the set of sentences of this language) much as embryology or physics deals with its subject matter. (Chomsky, 1964, p. 213)

   2) Native Speakers and Their Grammar

   Obviously, every speaker of a language has mastered and internalized a generative grammar that expresses his knowledge of his language. This is not to say that he is aware of the rules of grammar or even that he can become aware of them, or that his statements about his intuitive knowledge of his language are necessarily accurate. (Chomsky, 1965, p. 8)

   3) The Reduction of Transformation Rules In a Science of Grammar

   Much effort has been devoted to showing that the class of possible transformations can be substantially reduced without loss of descriptive power through the discovery of quite general conditions that all such rules and the representations they operate on and form must meet.... [The] transformational rules, at least for a substantial core grammar, can be reduced to the single rule, "Move alpha" (that is, "move any category anywhere"). (Mehler, Walker & Garrett, 1982, p. 21)

   4) The Relationship of Transformational Grammar to Semantics and to Human Performance

   he implications of assuming a semantic memory for what we might call "generative psycholinguistics" are: that dichotomous judgments of semantic well-formedness versus anomaly are not essential or inherent to language performance; that the transformational component of a grammar is the part most relevant to performance models; that a generative grammar's role should be viewed as restricted to language production, whereas sentence understanding should be treated as a problem of extracting a cognitive representation of a text's message; that until some theoretical notion of cognitive representation is incorporated into linguistic conceptions, they are unlikely to provide either powerful language-processing programs or psychologically relevant theories.

   Although these implications conﬂict with the way others have viewed the relationship of transformational grammars to semantics and to human performance, they do not eliminate the importance of such grammars to psychologists, an importance stressed in, and indeed largely created by, the work of Chomsky. It is precisely because of a growing interdependence between such linguistic theory and psychological performance models that their relationship needs to be clarified. (Quillian, 1968, p. 260)

   5) The Terminologies of Formal Grammar

   here are some terminological distinctions that are crucial to explain, or else confusions can easily arise. In the formal study of grammar, a language is defined as a set of sentences, possibly infinite, where each sentence is a string of symbols or words. One can think of each sentence as having several representations linked together: one for its sound pattern, one for its meaning, one for the string of words constituting it, possibly others for other data structures such as the "surface structure" and "deep structure" that are held to mediate the mapping between sound and meaning. Because no finite system can store an infinite number of sentences, and because humans in particular are clearly not pullstring dolls that emit sentences from a finite stored list, one must explain human language abilities by imputing to them a grammar, which in the technical sense is a finite rule system, or programme, or circuit design, capable of generating and recognizing the sentences of a particular language. This "mental grammar" or "psychogrammar" is the neural system that allows us to speak and understand the possible word sequences of our native tongue. A grammar for a specific language is obviously acquired by a human during childhood, but there must be neural circuitry that actually carries out the acquisition process in the child, and this circuitry may be called the language faculty or language acquisition device. An important part of the language faculty is universal grammar, an implementation of a set of principles or constraints that govern the possible form of any human grammar. (Pinker, 1996, p. 263)

   6) The Theory of Grammar

   A grammar of language L is essentially a theory of L. Any scientific theory is based on a finite number of observations, and it seeks to relate the observed phenomena and to predict new phenomena by constructing general laws in terms of hypothetical constructs.... Similarly a grammar of English is based on a finite corpus of utterances (observations), and it will contain certain grammatical rules (laws) stated in terms of the particular phonemes, phrases, etc., of English (hypothetical constructs). These rules express structural relations among the sentences of the corpus and the infinite number of sentences generated by the grammar beyond the corpus (predictions). (Chomsky, 1957, p. 49)

Historical dictionary of quotations in cognitive science > Grammar
13 language

язык || языковой

- absolute language
- action description language
- actual machine language
- agent programming language
- AI language
- Algol-like language
- algorithmical language
- algorithmic language
- application-oriented language
- applicative language
- artificial language
- assembler language
- assembly language
- assembly-output language
- assignment-free language
- behavioral language
- bidirectional language
- block-structured language
- Boolean-based language
- business definition language
- business-oriented language
- calculus-type language
- C-based language
- client-side language
- code language
- command language
- compiled language
- compiler language
- component definition language
- composite language
- computer language
- computer-dependent language
- computer-independent language
- computer-oriented language
- computer-programming language
- computer-sensitive language
- consensus language
- context-free language
- control language
- conversational language
- core language
- data definition language
- data description language
- data language
- data manipulation language
- data storage description language
- database language
- data-entry language
- data-flow language
- data-query language
- declarative language
- defining language
- descriptive language
- descriptor language
- design language
- device media control language
- direct execution language
- directly interpretable language
- Dyck language
- end-user language
- escape language
- evolutive language
- executive-control language
- executive language
- explicit language
- extensible language
- fabricated language
- finite state language
- flow language
- foreign language
- formalized language
- frame-based language
- freestanding language
- functional language
- generated language
- graphics language
- graph-oriented language
- hardware-description language
- hardware language
- higher-level language
- higher-order language
- host language
- human language
- human-oriented language
- human-readable language
- indexed language
- information retrieval language
- informational language
- information language
- inherently ambiguous language
- input language
- input/output language
- instruction language
- integrated language
- interactive language
- interim language
- intermediate language
- internal language
- interpreted language
- job control language
- job-oriented language
- knowledge representation language
- language pair
- letter-equivalent languages
- linear language
- linear-programming language
- list-processing language
- logic-type language
- low-level language
- machine language
- machine-dependent language
- machine-independent language
- machine-oriented language
- macroassembly language
- macro language
- macroinstruction language
- macroprogramming language
- man-to-computer language
- mathematical formular language
- memory management language
- mnemonic language
- modeling language
- native language
- natural language
- NC programming language
- nested language
- network-oriented language
- nonprocedural language
- numder language
- object language
- object modeling language
- object-oriented language
- one-dimensional language
- operator-oriented language
- original language
- page description language
- parallel language
- phrase structure language
- predicate language
- predicate logic-based language
- predicate logic language
- privacy language
- problem statement language
- problem-oriented language
- procedural language
- procedure-oriented language
- process control language
- production language
- program language
- programming language
- pseudo language
- pseudomachine language
- query language
- readable specification language
- reference language
- regular language
- relational language
- relational-type language
- representation language
- representation language

- requirement statement language
- requirements modeling language
- restricted language
- rule-based language
- ruly language
- schema language
- science-oriented language
- script language
- self-contained language
- semantic-formal language
- semiformal language
- sentential language
- serial language
- simulation language
- single-assignment language
- source language
- specialized language
- specification language
- stream-based language
- strict language
- structured programming language
- structured query language
- super language
- super-high-level language
- symbolic language
- symbolic programming language
- syntax language
- synthetic language
- system input language
- system language
- system-oriented language
- tabular language
- target language
- TC language
- time sharing language
- type-free language
- unified modeling language
- update language
- user language
- user-oriented language
- very-high-level language

English-Russian dictionary of computer science and programming > language
14 network

ˈnetwə:k сущ.
1) сеть, сетка;
плетенка
2) сеть а) (совокупность железных дорог, каналов и т. п.) communications network ≈ сеть коммуникаций, сеть связи, система связи road network ≈ сеть дорог network of rails ≈ сеть железных дорог community health network ≈ местная сеть здравоохранения (система медицинских учреждений, оказывающая помощь неимущим) б) взаимосвязанная цепочка или система нематериальных объектов;
схематическое представление событий и действий при изучении эффективности производственного процесса Network, a schematic representation of events and activities which shows their inter-relationships. ≈ Сеть - схематическое изображение событий и действий в их взаимосвязи. activity network ≈ сетевой график;
временная последовательность операций
3) тех. решетчатая система
4) радиотрансляционная сеть;
вещательная компания;
радиовещание, телевидение
5) электр. схема, цепь
6) взаимосвязанная группа людей, организация, сообщество I was paid about L500 for infiltrating the IRA network in London. ≈ Мне заплатили около 500 фунтов стерлингов за то, чтобы я проник в ряды ИРА в Лондоне. сеть, сетка плетенка сеточка, филе( в рукоделии) (текстильное) ажурное полотно плетение, вязание переплетение, хитросплетение;
сеть - * of intrigue сеть интриг - * of espionage шпионская сеть (электротехника) цепь, схема сеть (железнодорожная, канализационная, радиотрансляционная, телевизионная и т. п.) (техническое) решетчатая система (радиотехника) (телевидение) сетевое вещание - * announcer диктор (экономика) сетевой график( компьютерное) сеть - backbone * базовая сеть - ring * кольцевая сеть - * architecture архитектура сети ЭВМ - * data base сетевая база данных (американизм) покрывать сетью железных дорог - whole regions are *ed сетью железных дорог покрываются целые области( американизм) передавать, вещать по радио- или телевизионной сети( американизм) передаваться по радио- или телевизионной сети activity ~ сетевой график algorithmic ~ вчт. алгоритмическая сеть argumented transition ~ вчт. расширенная сеть переходов associative ~ вчт. ассоциативная сеть augmented transition ~ вчт. расширенная сеть переходов backbone ~ вчт. базовая сеть branch ~ сеть филиалов broadband ~ широкополосная сеть broadcast ~ широковещательная сеть causal ~ причинно-следственная сеть communications ~ сеть передачи данных communications ~ сеть связи computer ~ вычислительная сеть computer ~ компьютерная сеть computer ~ сеть вычислительных машин connectional ~ соединительная сеть data ~ вчт. сеть передачи данных dealer ~ торговая сеть distribution ~ распределительная сеть distribution ~ сеть распространения extensional semantic ~ семантическая экстенсиональная сеть hybrid ~ гибридная сеть inference ~ вчт. сеть вывода ladder ~ вчт. многозвенная схема local ~ вчт. локальная сеть network вычислительная сеть ~ радиотрансляционная сеть ~ тех. решетчатая система ~ сетевой график ~ сеть (железных дорог, каналов и т. п.) ~ сеть, сетка;
плетенка ~ вчт. сеть ~ сеть ~ сообщество ~ схема ~ эл. цепь, схема ~ data base вчт. сетевая база данных ~ file standard вчт. стандартный протокол сеевых файлов ~ file system вчт. сетевая файловая система ~ flow problem вчт. задача нахождения потока в сети ~ of queues вчт. многоканальная система обслуживания packet switching ~ вчт. сеть коммутации пакетов polled ~ вчт. сеть с опросом recursive transition ~ вчт. рекурсивная сеть переходов ring ~ вчт. кольцевая сеть road ~ дорожная сеть sales ~ сеть сбыта semantic ~ вчт. семантическая сеть slotted-ring ~ вчт. кольцевая сеть social ~ сеть социальных работников synchronous ~ вчт. синхронная сеть telecommunications ~ вчт. сеть связи telecommunications ~ система дальней связи telephone ~ телефонная сеть teleprinter ~ сеть телетайпной связи telex ~ система телексной связи terminal support ~ вчт. сеть поддержки терминалов traffic ~ транспортная сеть transition ~ вчт. сеть перехода состояний transport ~ вчт. транспортная сеть transport ~ транспортная сеть weighting ~ вчт. взвешивающая схема wideband ~ сеть связи с высокой пропускной способностью wideband ~ широкополосная сеть связи

Большой англо-русский и русско-английский словарь > network
15 equation

1. n книжн. выравнивание, уравнивание; уравновешивание

error equation — уравнивание ошибок; уравнивание погрешностей

2. n книжн. уравновешенность, правильное соотношение; согласованность

equation of demand and supply — равенство спроса и предложения

3. n книжн. приравнивание

personal equation — поправка на личные особенности, на характер

4. n книжн. хим. уравнение, формула реакции

semantic equation — семантическое уравнение

stability equation — уравнение остойчивости

transport equation — транспортное уравнение

unaffected equation — уравнение без аффекта

conservation equation — уравнение сохранения

Синонимический ряд:

symbolic representation (noun) chemical formula; empirical formula; formula; graphic formula; logarithm; ratio; recipe; symbolic representation; theorem

English-Russian base dictionary > equation
16 language

язык

-
absolute language
-
algorithmical language
-
algorithmic language
-
applicative language
-
artificial language
-
assembler language
-
block-structured language
-
Boolean algebra-based language
-
Boolean based language
-
command language
-
compilative language
-
compiler language
-
computer language
-
computer-dependent language
-
computer-independent language
-
computer-oriented language
-
computer-sensitive language
-
context-free language
-
control language
-
conversational language
-
core language
-
data language
- data manipulation language -
data-base language
-
data-definition language
-
data-query language
-
declarative language
-
deduction-oriented language
-
design language
-
explicit language
-
expression-oriented language
-
extensible language
-
FG-kernel language
-
finite state language
-
formal specification language
-
function language
-
functional language
-
graphics-oriented language
-
graphics language
-
hardware-based language
-
high-level language
-
host language
-
human language
-
human-oriented language
-
hybrid language
-
imperative language
-
input language
-
instruction language
-
interactive language
-
interface language
-
intermediate language
-
interpretive language
-
job control language
-
kernel language
-
knowledge representation language
-
list-processing language
-
low-level language
-
machine language
-
machine-dependent language
-
machine-independent language
-
machine-oriented language
-
macro language
-
meta language
-
mnemonic language
-
narrative language
-
native language
-
native-mode language
-
natural language
-
NC-AM language
-
network control language
-
nonprocedural language
-
nucleus language
-
object language
-
object-oriented language
-
original language
-
parallel language
-
plain language
-
privacy language
-
problem solving language
-
problem-oriented language
-
procedural language
-
program development language
-
program language
-
programming language
-
pseudo language
-
query language
-
real-time language
-
reference language
-
regular language
-
relational language
-
retrieval language
-
robot language
-
rule language
-
semantic language
-
sentential language
-
simulation language
-
source language
-
specification description language
-
specification language
-
stratified language
-
structured language
-
symbolic language
-
system language
-
system-oriented language
-
target language
-
typed language
-
unstratified language
-
untyped language
-
user-oriented language
-
world-modeling language

Англо-русский словарь технических терминов > language
17 matrix

1) мат. матрица || матричный

2) форма, шаблон

3) прямоугольная таблица

4) растр

5) решётка

6) таблица

7) геол. материнская порода, основная масса ( породы)

8) основа кристаллического вещества; кристаллическая решётка

•

adjoint of a matrix — сопряжённая матрица

in a matrix form — в матричной форме

proof by matrices — доказательство с помощью матриц

representation by matrices — матричное представление

to invert a matrix — обращать матрицу

to multiply a matrix on left — умножать матрицу слева

- absolutely bounded matrix
- absolutely equivalent matrices
- absorption matrix

- additively inverse matrix
- adjoint matrices
- adjoint matrix

- adjugate matrix

- admissible matrix

- alias matrix

- allocation matrix

- almost normal matrix
- almost perfect matrix - almost periodic matrix - almost superdiagonal matrix
- alternant matrix

- alternate matrix

- alternating matrix

- analytical matrix

- annihilating matrix

- antiself-dual matrix

- antisymmetric matrix

- antisymmetrical matrix

- aperiodic matrix

- associated matrix

- association matrix
- associative matrix
- asymtotic matrix

- augmented matrix

- autocorrelation matrix

- autocovariance matrix

- automaton matrix

- backcirculant matrix

- badly conditioned matrix

- basic matrix

- best conditioned matrix

- bidiagonal matrix

- bigradient matrix

- binary matrix

- biorthogonal matrices
- birth-death coefficient matrix
- block matrix

- block-diagonal matrix

- block-floating matrix

- block-periodic matrix

- block-stochastic matrix

- block-triangular matrix

- block-tridiagonal matrix

- bordered matrix

- boundary matrix

- bounded matrix

- canonical matrix

- capacity matrix

- cellular matrix

- centrosymmetrical matrix

- characteristic equation matrix

- characteristic matrix

- check matrix

- circuit matrix

- circuit-basis matrix

- clique matrix

- clique-incidence matrix

- cocircuit matrix
- cocycle matrix
- coding matrix

- cohesiveness matrix

- column definite matrix

- column-bounded matrix

- column-equilibrated matrix

- column-finite matrix

- column-infinite matrix

- column-reduced matrix

- combinatorial matrix

- combinatorially equivalent matrices
- commutative matrix - commute matrices - commuting matrix
- companion matrix

- comparable matrices

- compatibility matrix

- complete correlation matrix

- complete matrix

- completely reducible matrix
- complex conjugate matrix
- complex matrix

- complex orthogonal matrix

- component matrix

- composite matrix
- compound matrix
- conference matrix

- conformable matrices

- conformable matrix

- confounding matrix

- congruent matrices
- conjunctive matrices
- connected matrix

- connectedness matrix

- connection matrix

- conservative matrix

- consistent matrices
- consistently ordered matrix
- constant matrix

- constituent matrices matrix
- constituent matrices
- constraint matrix

- continuous matrix

- contraction matrix

- conversion matrix

- coordinatization matrix

- copositive matrix

- copositive-plus matrix

- correcting matrix

- cotree incidence matrix

- counterdiagonal matrix

- counteridentity matrix

- counterorthogonal matrices

- countersymmetrical matrix

- counterunitary matrix

- covariant matrix

- crossing matrix

- curvature matrix

- cutset basis matrix

- cutset matrix

- cyclic matrix

- cyclomatic matrix

- damping matrix

- decision matrix

- decoding matrix

- decomposable matrix

- decomposition matrix

- defective matrix
- deficient matrix
- degenerate matrix

- dense matrix

- derivative matrix

- derogatory matrix

- design matrix

- detour matrix

- deviation matrix

- diagnostic matrix

- diagonalizable matrix

- diagonalizing matrix

- diagonally dominant matrix

- differential matrix

- dilated matrix

- discontinuous matrix

- dispersion matrix

- distance matrix

- distribution matrix

- double diagonal matrix
- doubly degenerated matrix - doubly stochastic matrix
- dynamical matrix

- echelon matrix

- edge incident matrix

- edge matrix

- effectiveness matrix

- elementary matrix

- elimination matrix

- elliptic matrix

- encoder matrix
- encoding matrix
- equicofactor matrix

- equivalent matrices

- ergodic matrix

- essentially nonnegative matrix
- essentially positive matrix
- even matrix

- evolutional matrix

- exceptional matrix

- expansion matrix

- exponent matrix

- extended matrix

- fixed matrix

- flat type matrix

- flow matrix

- formal log-exponential matrix

- four-by-four matrix

- four-column matrix

- fully indecomposable matrix

- function of matrix

- fundamental matrix

- fuzzy matrix

- gains matrix

- general matrix

- generalized matrix

- graded matrix

- group matrix

- grouping matrix

- high-order matrix

- holomorphic matrix

- horizontally symmetrical matrix

- hyperbolic matrix

- hypercompanion matrix

- idempotent matrix

- identifiability matrix

- imaginary matrix

- immittance matrix

- imprimitive matrix

- improper matrix

- inconsistent matrices

- indecomposable matrix

- independent matrices

- ineffective matrix

- influence matrix

- integration matrix

- interblock matrix

- intersection matrix

- intrablock matrix

- invariant matrix

- invertible matrix

- involutive matrix

- irreducible matrix

- isotopic matrix

- iteration matrix

- jump matrix

- kinematically similar matrices

- lambda matrix

- leading matrix

- left-invariant matrix

- left-nonsingular matrix

- limiting matrix

- limiting transition matrix

- line matrix

- linearly dependent matrices

- linotype matrix

- logical conversion matrix

- logical matrix

- loop matrix

- loss matrix

- lower triangular matrix
- magnetic core matrix
- mapping matrix

- matrix of bilinear map

- matrix of coefficients

- matrix of constants

- matrix of linear map
- matrix of linear transformation
- matrix of observations

- matrix of orientation

- matrix of system

- matrix of wires

- m-by-n matrix

- m-column matrix

- mean-square error matrix

- measure matrix

- measurement error matrix

- meromorphic matrix

- minor definite matrix

- mixing matrix

- modal matrix

- model matrix

- mold matrix

- moment matrix

- monodromy matrix

- monomial matrix

- monotone matrix

- monotonicity matrix

- monotype matrix

- multidimensional matrix

- multiple-array matrix

- multiplicative inverse matrix
- multiplicatively cogredient matrix - multiply degenerate matrix - mutually consistent matrices
- n-by-n matrix

- n-column matrix

- nearly singular matrix
- nearly triangular matrix
- negative matrix

- negatively definite matrix
- negatively semidefinite matrix
- nilpotent matrix

- nil-triangular matrix

- node-arc incidence matrix

- nonconstant matrix

- nondegenerate matrix

- nonderogatory matrix

- nonexceptional matrix

- noninvertible matrix

- nonnegative matrix

- nonnegatively definite matrix
- nonnegatively semidefinite matrix
- nonnormal matrix

- nonorthogonal matrix

- nonpositive matrix

- nonrecurrent circulant matrix

- nonrecurrent matrix

- nonsymmetrical matrix

- non-unitary matrix

- nonvacuous matrix

- normalized matrix

- normed matrix

- n-rowed matrix

- n-th order matrix

- null matrix

- numerical matrix

- observability matrix

- observation matrix

- odd matrix

- one-by-three column matrix

- one-by-three row matrix

- one-step transition matrix

- operator matrix

- ordering matrix

- orthogonally equivalent matrices
- orthogonally similar matrices
- orthostochastic matrix

- oscillating matrix

- output matrix

- parabolic matrix

- paramount matrix

- parastrophic matrix

- parity-check matrix

- path matrix

- payoff matrix

- perfect matrix

- permutation matrix

- persymmetrical matrix

- pi matrix

- plain matrix

- polynomial matrix

- positive definite matrix
- positively definite matrix - positively semidefinite matrix
- precedence matrix

- preference matrix

- principal minor matrix

- probability matrix

- product matrix

- proximity matrix

- pseudoinverse matrix

- pseudoorthogonal matrix

- pseudoscalar matrix

- pseudosymplectic matrix

- pseudounitary matrix

- quadratic matrix

- qualification matrix

- quasicanonical matrix

- quasicommutative matrix

- quasidiagonal matrix

- quasidual matrix

- quasiinverse matrix

- quasi-inverse matrix

- quasiscalar matrix

- quasistochastic matrix

- quasisymmetrical matrix

- quasitriangular matrix

- quasitridiagonal matrix

- quaternionic matrix

- rational matrix

- reachability matrix

- real matrix

- reciprocal matrix

- rectangular matrix

- recursive matrix

- reflexion matrix

- regret matrix

- regular matrix

- regularly arranged matrix
- regularly partitioned matrix
- relational matrix

- residue matrix

- resolvent matrix

- reversible matrix

- right-invariant matrix

- right-nonsingular matrix

- rotation matrix

- row-bounded matrix

- row-equilibrated matrix

- row-finite matrix

- row-infinite matrix

- row-monomial matrix

- sandwich matrix

- scrambling matrix

- secondary matrix

- self-adjoint matrix

- self-conjugated matrix

- self-orthogonal matrix

- semantic matrix

- semicontinuous matrix

- semidefinite matrix

- semidiagonal matrix

- semiorthogonal matrix

- semisimple matrix

- semistable matrix

- shifting matrix

- signal matrix

- similar matrices

- similarity matrix

- simple classical matrix

- simplectic matrix

- simply degenerate matrix

- single-column matrix

- single-row matrix

- skeleton matrix

- skew matrix

- skew-symmetric matrix

- slightly intertwined matrices

- solenoidal matrix

- spectral density matrix

- spectral matrix

- spin matrix

- standardized matrix

- state-transition matrix

- statistical matrix

- steel matrix

- strictly triangular matrix
- strictly upper triangular matrix - strongly equivalent matrices
- structural matrix

- structure matrix

- structure of matrix

- subdiagonal matrix

- subinverse matrix

- substitution matrix

- substochastic matrix

- summation matrix

- superdiagonal matrix

- superinverse matrix

- superstochastic matrix

- symmetrized matrix

- system matrix

- system transfer matrix

- test matrix

- Toplitz matrix

- totally inefficient matrix
- totally nonnegative matrix - totally positive matrix - totally unimodular matrix
- traceless matrix

- transfer rate matrix

- transformed matrix

- transforming matrix

- transmission matrix

- trapezoidal matrix

- tree matrix

- triangulable matrix

- two-diagonal matrix

- two-dimensional matrix

- two-row matrix

- two-sided reciprocal matrix

- two-variable matrix

- unconditionally stable matrix
- uniformly tapered matrix
- unimodal matrix

- unimodular matrix

- union matrix

- unipotent matrix

- unitarily equivalent matrices

- unitary matrices

- unity matrix

- universal matrix

- unreduced matrix

- var-covar matrix

- variation diminishing matrix

- variation limiting matrix

- varying unitary matrix
- vertex incidence matrix
- vertex matrix

- vertex-edge incidence matrix
- vertically symmetrical matrix - weakly cyclic matrix
- weight matrix

- well-conditioned matrix

- zero matrix

English-Russian scientific dictionary > matrix
18 system

1) система; комплекс

2) совокупность

•

system in equilibrium — система в стационарном состоянии

- absolutely complete system
- absolutely consistent system - absolutely direct indecomposable system - absolutely free system - absolutely irreducible system - absolutely isolated system
- abstract system

- accumulator hydraulic system

- adaptive system

- additively closed system

- addressing system

- adjoint system

- affixative system

- aholonomic system

- aircraft electrical system

- airdrop system

- algebraically complete system
- allowable coordinate system - almost linear system - ample linear system
- ample system

- analytic system

- anholonomic system

- antenna system

- anticanonical system

- antiinvariant system

- antisymmetric system

- aperiodic system

- applicative system

- appointment system

- approach and docking system
- artificial feel system
- assertional system

- associated system

- associative system

- asymmetric system

- asymptotically stable system

- asynchronous system

- atomic system

- augmented system

- automated landing system
- automatic block system - automatic deicing system
- autonomous system

- axiomatic system

- axiomatizable system

- backward system

- balanced system

- base-ten system

- base-two system

- basic system

- betting system

- bicylindrical coordinate system

- bilinear system

- binary number system
- binary relational system - binary-coded decimal system
- binomial system

- biorthogonal system

- biorthonormal system

- bipolar coordinate system
- block tooling system
- block-recursive system

- branching system

- Brembo braking system

- bus system

- c.m. system

- carrier frequency system
- Cartesian coordinate system
- categorical system

- causal system

- cellular system

- center-of-mass system

- chance cause system

- characteristic system

- classical system

- classification system

- closed system

- closed-circuit television system

- closure system

- coarse system

- coaxial system of circles

- code system

- coding system

- cofinal system

- coherent system

- collection system

- combinatorial system

- commutation system

- complete system

- completely continuous system
- completely controllable system - completely ergodic system - completely hyperbolic system - completely identifiable system - completely integrable system - completely irreducible system - completely regular system - completely stable system - completely stratified system - complex number system
- complex system

- compound system

- computation system

- concatenative system

- conceptual system

- conditionally stable system
- conical coordinate system
- conjugate system

- conjunctive system

- connected system

- connecting system

- conservative system

- consistent system

- consistently interpretable system

- constant system

- constrained system

- continuous system

- continuously ordered system

- contragradient system

- control system

- controllable system

- controlled system

- convergence system

- converse system

- convex system

- cooling system

- correct formal system

- countable system

- counting system

- covariant local system

- creative system

- critical system

- crossbar system

- cryoelectronic system

- cybernetic system

- cycle matching system

- cyclic system

- data reduction system

- decadic system

- decidable system

- decision-making system

- decoding system

- decomposable system

- deductive system

- defective system of eigenvectors

- deflection system

- degenerate system

- denary system

- densely ordered system
- derivational formal system
- determinate system

- determined system

- determining system

- deterministic system

- diagonal system

- dial system

- dichotomous system

- difference equation system
- differential equation system - differential selsyn system - digital counting system - digital transmission system
- direct system

- directed system

- directly indecomposable system

- disconjugate system

- discrete system

- discrete-continuous system

- disjunctive system

- dispatch telephone system

- dissipative system

- distributed system

- dodecadic system

- doudecimal system

- draft system

- drainage system

- draw-in conduit system

- dual system

- duodecimal system

- duplex radio system

- dynamic system

- earth system

- elementarily equivalent system

- elementary system

- ellipsoidal coordinate system
- elliptic coordinate system - elliptic cylindrical coordinate system
- elliptic system

- encoder system

- energy system

- equational system

- equationally equivalent system

- equianharmonic system

- equicontinuous system

- equidistant system

- equivalence system

- equivalent system

- ergodic system

- error-controlled system

- essentially undecidable system

- eutactic system

- evaporator system

- exclusive system

- exponentially stable system
- externally inconsistent system
- extremal system

- extremally stable system

- feasible system

- fiber-optic communication system

- finder system

- finiitely definable system
- finite state system
- finite system

- finite-dimensional system

- finitely approximated system
- finitely axiomatizable system - finitely presented system
- finitizable system

- fixed-parameter system

- focusing system

- forced system

- formal system

- formalized system

- forward system

- free system

- fuel conveying system
- fully characteristic quotient system
- function system

- functional system

- fundamental system of neighborhoods
- fundamental system of solutions
- fuzzy system

- gas-cleaning system

- gas-pressurized system

- general system

- generalized system

- generating system

- grand system

- ground system

- hereditarily generating system

- heterogeneous system

- heuristic system

- hexadecimal number system

- hierarchy system

- history of system

- homogeneous system

- homoloidal system

- homomorphic system

- hot-water system

- hydraulic handling system
- hydraulic lift system
- hyperbolic system

- hypercomplex number system

- hypercomplex system

- hypoelliptic system

- hypothetical system

- ideal system

- idealized system

- imprimitive system

- imprimitivity system

- incidence system

- incompletable system

- incomplete system

- inconsistent system

- indecomposable system

- indexing system

- inductive system

- inequality system

- inertial system

- information retrieval system

- information system

- infravariant total system

- inhomogeneous system

- initial system

- inner system

- inteblock system

- integral system

- integrated manufacturing
- integrated switching system
- integrated system

- interconnected power system

- interpolation system

- interpretating system

- intuitionistic system

- inverse system

- invertible system

- involutory system

- irreducible system

- irreduciblly inconsistent system

- isolated system

- isometric system

- isomorphic system

- isomorphically embeddable system
- isomorphically embedded system
- isothermmic system

- iterative system

- jet engine starter system
- kernel normal system
- Korsch system

- laboratory system

- lacunary system

- ladder-type system

- lamellar system

- large system

- lattice system

- learning system

- left-handed coordinate system

- life-support system

- lightguiding system

- linear programming system
- linearly dependent system - linearly independent system - live hydraulic system
- local system

- locally embeddable system
- locking protection system
- logarithmic system

- logistic system

- loop carrier system

- lossless system

- lumped system

- majority voted system

- maximal system

- maximally overdetermined system
- meteor-burst communication system
- minimal system

- minimax system

- miniquaternion system

- modal system

- modular pneumatic-device system
- modular programming system
- modular system

- monogenic system

- monomorphic system

- monotonic system

- multiassertional system

- multidimensional system

- multilayer system

- multimodal system

- multiperiodic system

- multiple-data-set system

- multiple-input system

- multiplicative system

- multiplicatively closed system

- multiplier system

- multipoint distribution system

- multistable system

- multistage system

- multivariable system

- natural system

- neutral system

- nohereditary system

- nonassociative system

- nonautonomous system

- nonautonomous system

- noncritical system

- nondissipative system

- nonelementary system

- nonholonomic system

- nonlinear system

- nonoscillating system

- nonperiodic system

- nonrecursive system

- nonregular system

- nonsingular system

- nonstationary system

- n-order system

- normed system

- notation system

- n-state system

- null system

- number representation system

- number system

- numerical system

- observable system

- octal number system

- octal system

- one-loop system

- one-phase system

- open-flow intake system

- optimum system

- ordered system

- orthogonal coordinate system

- outer system

- overdetermined system

- oversimplified system

- packet transmission system

- pantactic system

- parabolic coordinate system
- parabolic cylindrical coordinate system
- parabolic system

- paraboloidal coordinate system

- parametric system

- partial system

- partially ordered system
- permanent four-wheel drive system
- perturbed system

- piece-rate system

- Pitot static system

- pluricanonical system

- pluriparabolic system

- polyhierarchical system

- polynominal-like system

- polystable system

- positional system

- pressure intake system

- prime system

- primitively elementary system

- probabilistic system

- projective coordinate system

- projective system

- prolate spheroidal coordinate system

- propulsion system

- prorepresentation system

- prototype system

- pseudodynamic system

- public address system

- pulsed navigation system
- pure independent system
- quadrophonic system

- quasicanonical system

- quasicomplete system

- quasi-electronic switching system

- quasiergodic system

- quasiharmonic system

- quasilinear system

- quasimultiplicative system

- quasiordered system

- quasireducible system

- quasistable system

- quasitriangular system

- queuing system

- quinary system

- quotient system

- radial ventilation system
- radio telephone system
- ramified system

- random system

- reactor control system
- reactor protection system - real number system - receiver-amplifier crioelectric system
- recovering system

- recurrent system

- reduced system

- redundant system

- regenerating system

- regularized system

- relational system

- relative system

- relatively isolated system
- remote-cylinder hydraulic system
- residual system

- resolving system

- restricted system

- retrospective system

- rheonomic system

- ride control system

- right-handed coordinate system

- right-handed system

- robot system

- root system

- rotating coordinate system

- safety-trip system

- sampled-data system

- satellite navigation system

- saturated system

- scanning system

- scleronomic system

- scoring system

- self-adaptive system

- self-adjoint system

- self-adjusting system

- self-contradictory system

- self-dual system

- self-excited system

- self-learning system

- self-optimizing system

- self-organizing system

- self-oscillating system

- semantic system

- semantically complete system
- semantically consistent system
- semidynamical system

- semilinear system

- semiordered system

- semiotic system

- semiregular coordinate system

- separable system

- sewage system

- sexadecimal system

- sexagesimal system

- shift system

- simply complete system
- simply consistent system - simply incomplete system - simply ordered system
- simulated system

- smooth system

- solvable system

- speed coding system
- spherical coordinate system
- spraying system

- sprinkling system

- stabilizable system

- staff system

- stand-by system

- star-like system

- start-stop system

- start-up system

- State System of Instruments

- state-determined system

- static system

- statistical system

- stochastic system

- stochastically connected system

- storage system

- stratified system

- strongly elliptic system
- strongly multiplicative system - structurally stable system - sufficiently general coordinate system
- summability system

- surjective system

- switching system

- syntactical system

- syntactically complete system

- synthesized system

- system of awards

- system of axioms

- system of calculation

- system of causes

- system of circles

- system of coefficients

- system of common representatives

- system of conditions

- system of coordinates

- system of distinct representatives

- system of eigenvalues

- system of equations

- system of factor groups
- system of frequency curves
- system of generators

- system of groups

- system of hypotheses

- system of imprimitivity

- system of independent estimations

- system of inequalities

- system of integers

- system of integrity

- system of lines

- system of logarithms

- system of matrices

- system of measures

- system of neighborhoods

- system of numeration

- system of parameters

- system of preferences

- system of proper functions

- system of pseudoequivalence

- system of random varables
- system of rational numbers
- system of reference

- system of representations

- system of residual classes

- system of sets

- system of simultaneous equations

- system of subgroups

- system of surfaces

- system of transition functions

- system of transitivity

- system of units

- system of weights

- system of wheels

- tag system

- tally system

- telegraph block system

- telephone system

- television relay system

- television system

- temperature control system

- ten-step system

- ternary system

- theorem-proving system

- time interval system
- time multiplex system - time-division multiplex system
- time-invariant system

- time-varying system

- timing system

- topologizing system

- toroidal coordinate system

- total system

- transient system

- transitivity system of group

- transmission system

- transposed system

- tree-like system

- triangulation system

- trichotomous system

- trichromatic system

- triclinic system

- trigonal system

- trigonometric system

- triorthogonal system

- triple system

- triple-orthogonal system

- trivial system

- truth-value system

- twelve-channel system

- two-level system

- two-particle system

- tyrefit system

- unattended system

- undecidable system

- underdetermined system

- unified radiotelemetry system
- uniformly complete system - univalent system of notation - universal system of notation
- unperturbed system

- unsaturated system

- unsolvable system

- unstable system

- variable-coefficient system

- vector system

- ventilation system

- very large system

- video system

- voting system

- warning system

- water utilization system

- water-sludge system

- weakly atomic system
- weakly closed system - weighted number system
- weighting system

- wellpoint system

- zero-order system

English-Russian scientific dictionary > system
19 DA
1. усилитель задержки
2. твёрдые радиоактивные отходы
3. оценка дозы облучения
4. настольный реквизит
5. клиентский счет “на усмотрение”
6. имеющиеся данные
7. запаздывающее действие
8. доступные данные
9. доступ по требованию
10. деаэратор
11. двойная амплитуда
12. атрибут данных (сети и системы связи)
13. аналого-цифровой (о преобразовании, преобразователе)
14. анализ с разрушением образца
15. адрес пункта назначения
16. адрес получателя
17. адрес назначения
18. адрес места назначения (получателя)
19. адаптация к дисперсии
20. агент обнаружения
агент обнаружения
Агент обнаружения работает в пространстве имен транспортной плоскости и обеспечивает разделение между этим пространством и именами плоскости управления. Федерации известны точки соединения (CP) и точки соединения окончания (TCP) в сети, в то время как местному DA известны только закрепленные за ним точки. (МСЭ-T G.709/ Y.1353).
[ http://www.iks-media.ru/glossary/index.html?glossid=2400324]

Тематики
- электросвязь, основные понятия
EN
- discovery agent
- DA
адаптация к дисперсии
(МСЭ-T G.691).
[ http://www.iks-media.ru/glossary/index.html?glossid=2400324]

Тематики
- электросвязь, основные понятия
EN
- dispersion accommodation
- DA
адрес места назначения (получателя)
(МСЭ-R F.1499).
[ http://www.iks-media.ru/glossary/index.html?glossid=2400324]

Тематики
- электросвязь, основные понятия
EN
- destination address
- DA
адрес назначения
(МСЭ-T G.8011.2/ Y.1307.2).
[ http://www.iks-media.ru/glossary/index.html?glossid=2400324]

Тематики
- электросвязь, основные понятия
EN
- destination address
- DA
адрес получателя
Информация, показывающая адрес получателя данных или пользователя услуг.
[ http://www.lexikon.ru/dict/net/index.html]

Тематики
- сети вычислительные
EN
- destination address
- DA
адрес пункта назначения
адрес получателя
—
[Л.Г.Суменко. Англо-русский словарь по информационным технологиям. М.: ГП ЦНИИС, 2003.]

Тематики
- информационные технологии в целом
Синонимы
- адрес получателя
EN
- destination address
- DA
анализ с разрушением образца
—
[А.С.Гольдберг. Англо-русский энергетический словарь. 2006 г.]

Тематики
- энергетика в целом
EN
- destructive analysis
- DA
аналого-цифровой (о преобразовании, преобразователе)
—
[А.С.Гольдберг. Англо-русский энергетический словарь. 2006 г.]

Тематики
- энергетика в целом
EN
- digital-to-analog
- da
атрибут данных (сети и системы связи)
Элемент данных, который задает имя (семантику), формат, диапазон возможных значений и представление значений при обращении.
[ ГОСТ Р 54325-2011 (IEC/TS 61850-2:2003)]

атрибут элемента данных
-
[ ГОСТ Р 54325-2011 (IEC/TS 61850-2:2003)]

EN

data attribute
defines the name (semantic), format, range of possible values, and representation of values while being communicated
[IEC 61850-2, ed. 1.0 (2003-08)]

Тематики
- релейная защита
Синонимы
- атрибут элемента данных
EN
- DA
- data attribute
двойная амплитуда
—
[Я.Н.Лугинский, М.С.Фези-Жилинская, Ю.С.Кабиров. Англо-русский словарь по электротехнике и электроэнергетике, Москва, 1999 г.]

Тематики
- электротехника, основные понятия
EN
деаэратор
Аппарат для удаления из воды растворённых кислорода и двуокиси углерода, вызывающих коррозию оборудования
[Терминологический словарь по строительству на 12 языках (ВНИИИС Госстроя СССР)]

Тематики
- водоснабжение и канализация в целом
EN
- da
- deaerator
DE
- Entgaser
- Entlüfter
FR
- désaérateur
доступ по требованию
—
[Л.Г.Суменко. Англо-русский словарь по информационным технологиям. М.: ГП ЦНИИС, 2003.]

Тематики
- информационные технологии в целом
EN
- demand access
- DA
доступные данные
имеющиеся данные
—
[Е.С.Алексеев, А.А.Мячев. Англо-русский толковый словарь по системотехнике ЭВМ. Москва 1993]

доступная информация
-
[Интент]

Тематики
- информационные технологии в целом
Синонимы
- доступная информация
- имеющиеся данные
EN
запаздывающее действие
Несвоевременность выполнения действия.
[А.С.Гольдберг. Англо-русский энергетический словарь. 2006 г.]

Тематики
- энергетика в целом
EN
имеющиеся данные
—
[А.С.Гольдберг. Англо-русский энергетический словарь. 2006 г.]

Тематики
- энергетика в целом
EN
- data available
- DA
клиентский счет “на усмотрение”
Счет, открытый клиентом на фондового брокера, фирму по торговле ценными бумагами или товарного брокера, который дает брокеру право совершать операции по покупке или продаже без предварительного согласия принципала за счет средств на этом счете. Принципалы обычно устанавливают лимиты по таким счетам, однако брокер располагает широкой свободой действий и должен лишь сообщать своему принципалу о совершенных покупках, продажах, убытках и прибылях и о стоимости портфеля инвестиций.
[ http://www.vocable.ru/dictionary/533/symbol/97]

Тематики
- финансы
EN
- discretionary account
- DA
настольный реквизит
Обслуживающие программы, представленные на рабочем столе.
[Гипертекстовый энциклопедический словарь по информатике Э. Якубайтиса]
[ http://www.morepc.ru/dict/]

Тематики
- информационные технологии в целом
EN
- DA
- Desk Accessory
оценка дозы облучения
—
[А.С.Гольдберг. Англо-русский энергетический словарь. 2006 г.]

Тематики
- энергетика в целом
EN
- dose assessment
- DA
твёрдые радиоактивные отходы
—
[А.С.Гольдберг. Англо-русский энергетический словарь. 2006 г.]

Тематики
- энергетика в целом
EN
усилитель задержки
—
[Я.Н.Лугинский, М.С.Фези-Жилинская, Ю.С.Кабиров. Англо-русский словарь по электротехнике и электроэнергетике, Москва, 1999 г.]

Тематики
- электротехника, основные понятия
EN
- delay amplifier
- DA
Англо-русский словарь нормативно-технической терминологии > DA
20 data attribute
1. атрибут данных (сети и системы связи)
2. атрибут данных
атрибут данных
Характеристика данных, например, длина, способ представления, необходимые для построения БД или массивов данных.
[ http://www.morepc.ru/dict/]

Тематики
- информационные технологии в целом
EN
- data attribute
атрибут данных (сети и системы связи)
Элемент данных, который задает имя (семантику), формат, диапазон возможных значений и представление значений при обращении.
[ ГОСТ Р 54325-2011 (IEC/TS 61850-2:2003)]

атрибут элемента данных
-
[ ГОСТ Р 54325-2011 (IEC/TS 61850-2:2003)]

EN

data attribute
defines the name (semantic), format, range of possible values, and representation of values while being communicated
[IEC 61850-2, ed. 1.0 (2003-08)]

Тематики
- релейная защита
Синонимы
- атрибут элемента данных
EN
- DA
- data attribute
Англо-русский словарь нормативно-технической терминологии > data attribute

См. также в других словарях:

Semantic network — A Semantic network is a network, which represents semantic relations between the concepts. This is often used as a form of knowledge representation. It is a directed or undirected graph consisting of vertices, which represent concepts, and edges … Wikipedia
Semantic memory — refers to the memory of meanings, understandings, and other concept based knowledge unrelated to specific experiences. The conscious recollection of factual information and general knowledge about the world,cite web… … Wikipedia
Semantic interoperability — (also referred to as Computable Semantic Interoperability) is the ability of two or more computer systems to exchange information and have the meaning of that information automatically interpreted by the receiving system accurately enough to… … Wikipedia
Semantic-oriented programming — (SOP) in which you express your code directly in semantic meanings, most suitable to reflect your task. This means, that for each task you may need to add new semantic meanings, thus you ll need an extendable and configurable programming language … Wikipedia
Semantic translation — is the process of using semantic information to aid in the translation of data in one representation or data model to another representation or data model. Semantic translation takes advantage of semantics that associate meaning with individual… … Wikipedia
Semantic similarity — or semantic relatedness is a concept whereby a set of documents or terms within term lists are assigned a metric based on the likeness of their meaning / semantic content. Concretely, this can be achieved for instance by defining a topological… … Wikipedia
Semantic MediaWiki — Тип Расширение Разработчик Технологический институт … Википедия
Semantic analysis — may refer to: *Semantic analysis (compilers) *Semantic analysis (machine learning) *Semantic analysis (knowledge representation) *Semantic analysis (linguistics) … Wikipedia
Semantic gap — The semantic gap characterizes the difference between two descriptions of an object by different linguistic representations, for instance languages or symbols. In computer science, the concept is relevant whenever ordinary human activities,… … Wikipedia
Semantic spectrum — The semantic spectrum (sometimes referred to as the ontology spectrum or the smart data continuum or semantic precision) is a series of increasingly precise or rather semantically expressive definitions for data elements in knowledge… … Wikipedia
Semantic data model — A semantic data model in software engineering is a data modeling technique to define the meaning of data within the context of its interrelationships with other data. A semantic data model is an abstraction which defines how the stored symbols… … Wikipedia

Словари и энциклопедии на Академике

Перевод: с английского на все языки

semantic representation

Тематики

EN

Тематики

EN

Тематики

EN

Тематики

EN

Тематики

EN

Тематики

EN

Тематики

EN

Тематики

Синонимы

EN

Тематики

EN

Тематики

EN

Тематики

Синонимы

EN

Тематики

EN

Тематики

EN

DE

FR

Тематики

EN

Тематики

Синонимы

EN

Тематики

EN

Тематики

EN

Тематики

EN

Тематики

EN

Тематики

EN

Тематики

EN

Тематики

EN

Тематики

EN

Тематики

Синонимы

EN

См. также в других словарях:

Поделиться ссылкой на выделенное

Прямая ссылка: