Search results for 'Machine theory' (try it on Scholar)

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  1. Ave Mets (2013). Measurement Theory, Nomological Machine And Measurement Uncertainties (In Classical Physics). Studia Philosophica Estonica 5 (2):167-186.
    Measurement is said to be the basis of exact sciences as the process of assigning numbers to matter (things or their attributes), thus making it possible to apply the mathematically formulated laws of nature to the empirical world. Mathematics and empiria are best accorded to each other in laboratory experiments which function as what Nancy Cartwright calls nomological machine: an arrangement generating (mathematical) regularities. On the basis of accounts of measurement errors and uncertainties, I will argue for two claims: (...)
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  2.  4
    Robert Shaw & James Todd (1980). Abstract Machine Theory and Direct Perception. Behavioral and Brain Sciences 3 (3):400.
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  3.  8
    Frederick B. Churchill (1969). From Machine-Theory to Entelechy: Two Studies in Developmental Teleology. [REVIEW] Journal of the History of Biology 2 (1):165 - 185.
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  4.  1
    Frederick B. Churchill (1969). From Machine-Theory to Entelechy: Two Studies in Developmental Teleology. Journal of the History of Biology 2 (1):165-185.
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  5.  40
    Shalom Lappin, Machine Learning Theory and Practice as a Source of Insight Into Universal Grammar.
    In this paper, we explore the possibility that machine learning approaches to naturallanguage processing being developed in engineering-oriented computational linguistics may be able to provide specific scientific insights into the nature of human language. We argue that, in principle, machine learning results could inform basic debates about language, in one area at least, and that in practice, existing results may offer initial tentative support for this prospect. Further, results from computational learning theory can inform arguments carried on (...)
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  6.  18
    StuartmShieber, Machine Learning Theory and Practice as a Source of Insight Into Universal Grammar.
    In this paper, we explore the possibility that machine learning approaches to naturallanguage processing being developed in engineering-oriented computational linguistics may be able to provide specific scientific insights into the nature of human language. We argue that, in principle, machine learning results could inform basic debates about language, in one area at least, and that in practice, existing results may offer initial tentative support for this prospect. Further, results from computational learning theory can inform arguments carried on (...)
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  7.  18
    P. Cariani (2012). Mind, a Machine? Review of “The Search for a Theory of Cognition: Early Mechanisms and New Ideas” Edited by Stefano Franchi and Francesco Bianchini. Constructivist Foundations 7 (3):222-227.
    Upshot: Written by recognized experts in their fields, the book is a set of essays that deals with the influences of early cybernetics, computational theory, artificial intelligence, and connectionist networks on the historical development of computational-representational theories of cognition. In this review, I question the relevance of computability arguments and Jonasian phenomenology, which has been extensively invoked in recent discussions of autopoiesis and Ashby’s homeostats. Although the book deals only indirectly with constructivist approaches to cognition, it is useful reading (...)
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  8.  39
    Albert E. Lyngzeidetson & Martin K. Solomon (1994). Abstract Complexity Theory and the Mind-Machine Problem. British Journal for the Philosophy of Science 45 (2):549-54.
    In this paper we interpret a characterization of the Gödel speed-up phenomenon as providing support for the ‘Nagel-Newman thesis’ that human theorem recognizers differ from mechanical theorem recognizers in that the former do not seem to be limited by Gödel's incompleteness theorems whereas the latter do seem to be thus limited. However, we also maintain that (currently non-existent) programs which are open systems in that they continuously interact with, and are thus inseparable from, their environment, are not covered by the (...)
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  9.  19
    Timothy Shanahan (2008). Why Don't Zebras Have Machine Guns Adaptation, Selection, and Constraints in Evolutionary Theory. Studies in History and Philosophy of Science Part C 39 (1):135-146.
    In an influential paper, Stephen Jay Gould and Richard Lewontin contrasted selection-driven adaptation with phylogenetic, architectural, and developmental constraints as distinct causes of phenotypic evolution. In subsequent publications Gould has elaborated this distinction into one between a narrow “Darwinian Fundamentalist” emphasis on “external functionalist” processes, and a more inclusive “pluralist” emphasis on “internal structuralist” principles. Although theoretical integration of functionalist and structuralist explanations is the ultimate aim, natural selection and internal constraints are treated as distinct causes of evolutionary change. This (...)
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  10. Allen Newell & Herbert A. Simon (1956). The Logic Theory Machine -- A Complex Information Processing System. IRE Transactions on Information Theory 2 (3):61--79.
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  11.  97
    Nicholas Gane (2006). Book Review: Beyond the Image Machine: A History of Visual Technologies; Critical Technology: A Social Theory of Personal Computing. [REVIEW] Thesis Eleven 84 (1):141-144.
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  12.  21
    Valerie Tiberius (2013). Beyond the Experience Machine: How to Build a Theory of Well-Being. In Matthew C. Haug (ed.), Philosophical Methodology: The Armchair or the Laboratory? Routledge 398.
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  13.  2
    Allen Newell & Herbert A. Simon (1957). The Logic Theory Machine. A Complex Information Processing System. Journal of Symbolic Logic 22 (3):331-332.
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  14. Timothy Shanahan (2008). Why Don’T Zebras Have Machine Guns? Adaptation, Selection, and Constraints in Evolutionary Theory. Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences 39 (1):135-146.
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  15.  1
    Abraham Robinson (1962). Newell A., Shaw J. C., and Simon H. A.. Empirical Explorations of the Logic Theory Machine: A Case Study in Heuristic. Proceedings of the Western Joint Computer Conference, Los Angeles 1957, Pp. 218–230.Cowan Bryan, McClurg G. H., Newell A., Tanner P. E., Yarbrough L. D.. Discussion. Proceedings of the Western Joint Computer Conference, Los Angeles 1957, P. 230. [REVIEW] Journal of Symbolic Logic 27 (1):102-103.
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  16.  28
    Shalom Lappin with S. Shieber, Machine Learning Theory and Practice as a Source of Insight Into Universal Grammar.
  17.  2
    Lawrence C. Paulson (2014). A Machine-Assisted Proof of Gödel’s Incompleteness Theorems for the Theory of Hereditarily Finite Sets. Review of Symbolic Logic 7 (3):484-498.
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  18.  4
    José Rey de Castro (2013). ¿Es el cuerpo humano Una máquina nerviosa? La teoría Del cuerpo de Merleau-ponty Ante Los desafíos de la sociedad tecnológica / is the human body a nervous machine? Body theory of Merleau-ponty facing the challenges of the technological society. Synesis 5 (2):100-112.
    Este trabajo explora en algunas obras de Maurice Merleau-Ponty la posibilidad de considerar el cuerpo como una máquina nerviosa, tomando como marco su crítica a la mentalidad cientificista y positivista. Para cumplir con este propósito se estudia la relación entre el cuerpo y el mundo, dando particular atención a las reflexiones vinculadas a la ciencia y la técnica. Las obras que serán protagonistas en esta exploración son La fenomenología de la percepción (1945), El mundo de la percepción (conjunto de conferencias (...)
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  19. Andrzej Ehrenfeucht (1957). Review: Allen Newell, Herbert A. Simon, The Logic Theory Machine. A Complex Information Processing System. [REVIEW] Journal of Symbolic Logic 22 (3):331-332.
     
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  20.  3
    Abraham Robinson (1962). Review: A. Newell, J. C. Shaw, Programming the Logic Theory Machine. [REVIEW] Journal of Symbolic Logic 27 (1):103-103.
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  21.  3
    José Augusto Rey de Castro (2013). ¿Es el cuerpo humano Una máquina nerviosa? La teoría Del cuerpo de Merleau-ponty Ante Los desafíos de la sociedad tecnológica / is the human body a nervous machine? Body theory of Merleau-ponty facing the challenges of the technological society. Synesis 5 (2):100-112.
    Este trabajo explora en algunas obras de Maurice Merleau-Ponty la posibilidad de considerar el cuerpo como una máquina nerviosa, tomando como marco su crítica a la mentalidad cientificista y positivista. Para cumplir con este propósito se estudia la relación entre el cuerpo y el mundo, dando particular atención a las reflexiones vinculadas a la ciencia y la técnica. Las obras que serán protagonistas en esta exploración son La fenomenología de la percepción (1945), El mundo de la percepción (conjunto de conferencias (...)
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  22.  2
    C. E. M. Yates (1996). Rogers Hartley Jr., The Present Theory of Turing Machine Computability. Journal of the Society for Industrial and Applied Mathematics, Vol. 7 (1959), Pp. 114–130. [REVIEW] Journal of Symbolic Logic 31 (3):513-513.
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  23.  2
    Stephen A. Cook (1969). Review: Manuel Blum, A Machine-Independent Theory of the Complexity of Recursive Functions. [REVIEW] Journal of Symbolic Logic 34 (4):657-658.
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  24.  1
    Abraham Robinson (1962). Review: A. Newell, J. C. Shaw, H. A. Simon, Empirical Explorations of the Logic Theory Machine: A Case Study in Heuristic. [REVIEW] Journal of Symbolic Logic 27 (1):102-103.
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  25. José Rey de Castro (2013). Is the Human Body a Nervous Machine?: Body Theory of Merleau-Ponty Facing the Challenges of the Technological Society. Synesis 5 (2):100-112.
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  26. I. Bernard Cohen (1981). Current BooksAepinus's Essay on the Theory of Electricity and MagnetismAepinus P. J. ConnorElectricity From Glass: The History of the Frictional Electrical Machine 1600-1850Willem D. HackmannElectricity in the 17th & 18th Centuries: A Study of Early Modern PhysicsJ. L. Heilbron. [REVIEW] Isis 72 (3):480-489.
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  27. Stephen A. Cook (1969). Blum Manuel. A Machine-Independent Theory of the Complexity of Recursive Functions. Journal of the Association for Computing Machinery, Vol. 14 , Pp. 322–336. [REVIEW] Journal of Symbolic Logic 34 (4):657-658.
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  28. Andrzej Ehrenfeucht (1957). Newell Allen and Simon Herbert A.. The Logic Theory Machine. A Complex Information Processing System. Institute of Radio Engineers, Transactions on Information Theory, Vol. IT-2 No. 3 , Pp. 61–79. [REVIEW] Journal of Symbolic Logic 22 (3):331-332.
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  29. H. B. Enderton (1975). Lin Shen and Rado Tibor. Computer Studies of Turing Machine Problems. Journal of the Association for Computing Machinery, Vol. 12 , Pp. 196–212.Brady Allen H.. The Conjectured Highest Scoring Machines for Rado's Σ for the Value K = 4. IEEE Transactions on Electronic Computers, Vol. EC-15 , Pp. 802–803.Green Milton W.. A Lower Bound on Rado's Sigma Function for Binary Turing Machines. Switching Circuit Theory and Logical Design, Proceedings of the Fifth Annual Symposium, Princeton University, Princeton, N.J., November 11-13, 1964, The Institute of Electrical and Electronics Engineers, Inc., New York 1964, Pp. 91–94. [REVIEW] Journal of Symbolic Logic 40 (4):617.
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  30. Joyce Friedman (1967). Dunham B., Fridshal R., and Sward G. L.. A Non-Heuristic Program for Proving Elementary Logical Theorems. English, with French, German, Russian, and Spanish Summaries. Information Processing, Proceedings of the International Conference on Information Processing, Unesco, Paris 15–20 June 1959, Unesco, Paris, R. Oldenbourg, Munich, and Butterworths, London, 1960, Pp. 282–285.Dunham B., Fridshal R., and North J. H.. Exploratory Mathematics by Machine. Recent Developments in Information and Decision Processes, Edited by Machol Robert E. And Gray Paul, The Macmillan Company, New York 1962, Pp. 149–160.Dunham B. And North J. H.. Theorem Testing by Computer. Proceedings of the Symposium on Mathematical Theory of Automata, New York, N. Y., April 24, 25, 26, 1962, Microwave Research Symposia Series Vol. 12, Polytechnic Press of the Polytechnic Institute of Brooklyn, Brooklyn, N.Y., 1963, Pp. 173–177. [REVIEW] Journal of Symbolic Logic 32 (2):266.
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  31. Pa Heelan (1986). Machine Perception in Philosophy and Technology II. Information Technology and Computers in Theory and Practice. Boston Studies in the Philosophy of Science 90:131-156.
     
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  32. Abraham Robinson (1962). Newell A. And Shaw J. C.. Programming the Logic Theory Machine. Proceedings of the Western Joint Computer Conference, Los Angeles 1957, Pp. 230–240.Matlock J., Meissner L. P., Sayre P., and Shaw J. C.. Discussion. Proceedings of the Western Joint Computer Conference, Los Angeles 1957, P. 240. [REVIEW] Journal of Symbolic Logic 27 (1):103.
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  33. Jd Sneed (1989). Machine Models for the Growth of Knowledge: Theory Nets in Prolog in Imre Lakatos and Theories of Scientific Change. Boston Studies in the Philosophy of Science 111:245-268.
     
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  34. Iain A. Stewart (1996). The Demise of the Turing Machine in Complexity Theory. In P. J. R. Millican & A. Clark (eds.), Machines and Thought: The Legacy of Alan Turing, Volume 1. Clarendon Press
     
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  35. Akifumi Tokosumi (2001). A Computational Literary Theory: The Ultimate Products of the Brain/Mind Machine. In T. Kitamura (ed.), What Should Be Computed to Understand and Model Brain Function? World Scientific 3--43.
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  36. C. E. M. Yates (1966). Review: Hartley Rogers, The Present Theory of Turing Machine Computability. [REVIEW] Journal of Symbolic Logic 31 (3):513-513.
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  37. C. E. M. Yates & Hartley Rogers (1966). The Present Theory of Turing Machine Computability. Journal of Symbolic Logic 31 (3):513.
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  38. Shan Gao (2008). A Quantum Theory of Consciousness. Minds and Machines 18 (1):39-52.
    The relationship between quantum collapse and consciousness is reconsidered under the assumption that quantum collapse is an objective dynamical process. We argue that the conscious observer can have a distinct role from the physical measuring device during the process of quantum collapse owing to the intrinsic nature of consciousness; the conscious observer can know whether he is in a definite state or a quantum superposition of definite states, while the physical measuring device cannot “know”. As a result, the consciousness observer (...)
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  39.  27
    Edwin J. Beggs, José Félix Costa & John V. Tucker (2010). Physical Oracles: The Turing Machine and the Wheatstone Bridge. Studia Logica 95 (1/2):279 - 300.
    Earlier, we have studied computations possible by physical systems and by algorithms combined with physical systems. In particular, we have analysed the idea of using an experiment as an oracle to an abstract computational device, such as the Turing machine. The theory of composite machines of this kind can be used to understand (a) a Turing machine receiving extra computational power from a physical process, or (b) an experimenter modelled as a Turing machine performing a test (...)
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  40.  1
    S. Iu Maslov, Michael Gelfond & Vladimir Lifschitz (1987). Theory of Deductive Systems and its Applications. Monograph Collection (Matt - Pseudo).
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  41.  1
    Harry R. Lewis & Christos H. Papadimitriou (1984). Elements of the Theory of Computation. Journal of Symbolic Logic 49 (3):989-990.
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  42. Roy Harris (1987). The Language Machine. Cornell University Press.
  43. Marek Karpiński (ed.) (1977). Fundamentals of Computation Theory: Proceedings of the 1977 International Fct-Conference, Poznán-Kórnik, Poland, September 19-23, 1977. [REVIEW] Springer-Verlag.
     
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  44. Harry R. Lewis & Christos H. Papadimitriou (1998). Elements of the Theory of Computation Harry R. Lewis, Christos H. Papadimitriou.
     
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  45.  1
    Bernhard Schölkopf (2003). Statistical Learning Theory, Capacity, and Complexity. Complexity 8 (4):87-94.
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  46.  55
    Wendell Wallach, Stan Franklin & Colin Allen (2010). A Conceptual and Computational Model of Moral Decision Making in Human and Artificial Agents. Topics in Cognitive Science 2 (3):454-485.
    Recently, there has been a resurgence of interest in general, comprehensive models of human cognition. Such models aim to explain higher-order cognitive faculties, such as deliberation and planning. Given a computational representation, the validity of these models can be tested in computer simulations such as software agents or embodied robots. The push to implement computational models of this kind has created the field of artificial general intelligence (AGI). Moral decision making is arguably one of the most challenging tasks for computational (...)
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  47.  34
    Malcolm R. Forster (1999). How Do Simple Rules `Fit to Reality' in a Complex World? Minds and Machines 9 (4):543-564.
    The theory of fast and frugal heuristics, developed in a new book called Simple Heuristics that make Us Smart (Gigerenzer, Todd, and the ABC Research Group, in press), includes two requirements for rational decision making. One is that decision rules are bounded in their rationality –- that rules are frugal in what they take into account, and therefore fast in their operation. The second is that the rules are ecologically adapted to the environment, which means that they `fit to (...)
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  48.  26
    Kay Herrmann (2014). Faszination Zeitreisen. Universitätsverlag Chemnitz.
    Time travel is one of mankind's most ancient dreams. It inspires our imagination and provides material for bizarre stories. H. G. Wells' novel, "The Time Machine" (1895), marks the beginning of a long history of science fiction literature devoted to the subject of time travel. -/- A work on the subject of time travel forces us to re-examine our concept of "time". The complexity and the contradictory nature this subject makes it difficult to be more precise about "time". On (...)
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  49.  67
    Gary Hatfield (2007). The Passions of the Soul and Descartes's Machine Psychology. Studies in History and Philosophy of Science Part A 38 (1):1-35.
    Descartes developed an elaborate theory of animal physiology that he used to explain functionally organized, situationally adapted behavior in both human and nonhuman animals. Although he restricted true mentality to the human soul, I argue that he developed a purely mechanistic (or material) ‘psychology’ of sensory, motor, and low-level cognitive functions. In effect, he sought to mechanize the offices of the Aristotelian sensitive soul. He described the basic mechanisms in the Treatise on man, which he summarized in the Discourse. (...)
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  50.  74
    Shane Legg & Marcus Hutter (2007). Universal Intelligence: A Definition of Machine Intelligence. Minds and Machines 17 (4):391-444.
    A fundamental problem in artificial intelligence is that nobody really knows what intelligence is. The problem is especially acute when we need to consider artificial systems which are significantly different to humans. In this paper we approach this problem in the following way: we take a number of well known informal definitions of human intelligence that have been given by experts, and extract their essential features. These are then mathematically formalised to produce a general measure of intelligence for arbitrary machines. (...)
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