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.  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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  3.  7
    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
    Robert Shaw & James Todd (1980). Abstract Machine Theory and Direct Perception. Behavioral and Brain Sciences 3 (3):400.
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  5.  34
    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.  14
    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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  7.  10
    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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  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.  86
    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.  19
    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. 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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  14. 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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  15.  1
    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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  16.  25
    Shalom Lappin with S. Shieber, Machine Learning Theory and Practice as a Source of Insight Into Universal Grammar.
  17.  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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  18.  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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  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.  2
    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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  21.  1
    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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  22.  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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  23.  1
    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. 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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  25. 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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  26. 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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  27. 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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  28. 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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  29. 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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  30.  19
    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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  31. S. Iu Maslov, Michael Gelfond & Vladimir Lifschitz (1987). Theory of Deductive Systems and its Applications. Monograph Collection (Matt - Pseudo).
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  32. 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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  33. Roy Harris (1987). The Language Machine. Cornell University Press.
  34. 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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  35. Harry R. Lewis & Christos H. Papadimitriou (1998). Elements of the Theory of Computation Harry R. Lewis, Christos H. Papadimitriou.
     
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  36.  1
    Bernhard Schölkopf (2003). Statistical Learning Theory, Capacity, and Complexity. Complexity 8 (4):87-94.
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  37.  33
    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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  38.  25
    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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  39.  21
    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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  40.  61
    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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  41.  65
    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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  42.  81
    Johnjoe McFadden (2013). The CEMI Field Theory Closing the Loop. Journal of Consciousness Studies 20 (1-2):1-2.
    Several theories of consciousness first described about a decade ago, including the conscious electromagnetic information (CEMI) field theory, claimed that the substrate of consciousness is the brain’s electromagnetic (EM) field. These theories were prompted by the observation, in many diverse systems, that synchronous neuronal firing, which generates coherent EM fields, was a strong correlate of attention, awareness, and consciousness. However, when these theories were first described there was no direct evidence that synchronous firing was actually functional, rather than an (...)
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  43.  41
    Nigel Cutland (1980). Computability, an Introduction to Recursive Function Theory. Cambridge University Press.
    What can computers do in principle? What are their inherent theoretical limitations? These are questions to which computer scientists must address themselves. The theoretical framework which enables such questions to be answered has been developed over the last fifty years from the idea of a computable function: intuitively a function whose values can be calculated in an effective or automatic way. This book is an introduction to computability theory (or recursion theory as it is traditionally known to mathematicians). (...)
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  44.  9
    Dennis P. Waters (2012). Von Neumann's Theory of Self-Reproducing Automata: A Useful Framework for Biosemiotics? Biosemiotics 5 (1):5-15.
    As interpreted by Pattee, von Neumann’s Theory of Self-Reproducing Automata has proved to be a useful tool for understanding some of the difficulties and paradoxes of molecular biosemiotics. But is its utility limited to molecular systems or is it more generally applicable within biosemiotics? One way of answering that question is to look at the Theory as a model for one particular high-level biosemiotic activity, human language. If the model is not useful for language, then it certainly cannot (...)
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  45.  16
    Lisa D. Bendixen & Florian C. Feucht (eds.) (2010). Personal Epistemology in the Classroom: Theory, Research, and Implications for Practice. Cambridge University Press.
    Machine generated contents note: Part I. Introduction: 1. Personal epistemology in the classroom: a welcome and guide for the reader Florian C. Feucht and Lisa D. Bendixen; Part II. Frameworks and Conceptual Issues: 2. Manifestations of an epistemological belief system in pre-k to 12 classrooms Marlene Schommer-Aikins, Mary Bird, and Linda Bakken; 3. Epistemic climates in elementary classrooms Florian C. Feucht; 4. The integrative model of personal epistemology development: theoretical underpinnings and implications for education Deanna C. Rule and Lisa (...)
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  46.  7
    Merlin Carl, Tim Fischbach, Peter Koepke, Russell Miller, Miriam Nasfi & Gregor Weckbecker (2010). The Basic Theory of Infinite Time Register Machines. Archive for Mathematical Logic 49 (2):249-273.
    Infinite time register machines (ITRMs) are register machines which act on natural numbers and which are allowed to run for arbitrarily many ordinal steps. Successor steps are determined by standard register machine commands. At limit times register contents are defined by appropriate limit operations. In this paper, we examine the ITRMs introduced by the third and fourth author (Koepke and Miller in Logic and Theory of Algorithms LNCS, pp. 306–315, 2008), where a register content at a limit time (...)
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  47.  47
    Andrew A. Fingelkurts, Alexander A. Fingelkurts & Carlos F. H. Neves (2012). Machine” Consciousness and “Artificial” Thought: An Operational Architectonics Model Guided Approach. Brain Research 1428:80-92.
    Instead of using low-level neurophysiology mimicking and exploratory programming methods commonly used in the machine consciousness field, the hierarchical Operational Architectonics (OA) framework of brain and mind functioning proposes an alternative conceptual-theoretical framework as a new direction in the area of model-driven machine (robot) consciousness engineering. The unified brain-mind theoretical OA model explicitly captures (though in an informal way) the basic essence of brain functional architecture, which indeed constitutes a theory of consciousness. The OA describes the neurophysiological (...)
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    Michael J. Shaffer (2009). Decision Theory, Intelligent Planning and Counterfactuals. Minds and Machines 19 (1):61-92.
    The ontology of decision theory has been subject to considerable debate in the past, and discussion of just how we ought to view decision problems has revealed more than one interesting problem, as well as suggested some novel modifications of classical decision theory. In this paper it will be argued that Bayesian, or evidential, decision-theoretic characterizations of decision situations fail to adequately account for knowledge concerning the causal connections between acts, states, and outcomes in decision situations, and so (...)
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  49.  11
    Valery M. Tsourikov (1993). Inventive Machine: Second Generation. [REVIEW] AI and Society 7 (1):62-77.
    Inventive Machine project is the matter of discussion. The project aims to develop a family of AI systems for intelligent support of all stages of engineering design.Peculiarities of the IM project:deep and comprehensive knowledge base — the theory of inventive problem solving (TIPS)solving complex problems at the level of inventionsapplication in any area of engineeringstructural prediction of engineering system developmentThe systems of the second generation are described in detail.
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  50.  14
    Benjamin Wells (2002). Is There a Nonrecursive Decidable Equational Theory? Minds and Machines 12 (2):301-324.
    The Church-Turing Thesis (CTT) is often paraphrased as ``every computable function is computable by means of a Turing machine.'' The author has constructed a family of equational theories that are not Turing-decidable, that is, given one of the theories, no Turing machine can recognize whether an arbitrary equation is in the theory or not. But the theory is called pseudorecursive because it has the additional property that when attention is limited to equations with a bounded number (...)
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