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.score: 126.0
    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. Shalom Lappin, Machine Learning Theory and Practice as a Source of Insight Into Universal Grammar.score: 96.0
    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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  3. 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.score: 96.0
    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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  4. StuartmShieber, Machine Learning Theory and Practice as a Source of Insight Into Universal Grammar.score: 96.0
    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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  5. Shan Gao (2008). A Quantum Theory of Consciousness. Minds and Machines 18 (1):39-52.score: 92.0
    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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  6. 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.score: 90.0
    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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  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.score: 90.0
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  8. Rajesh K. Kana Gopikrishna Deshpande, Lauren E. Libero, Karthik R. Sreenivasan, Hrishikesh D. Deshpande (2013). Identification of Neural Connectivity Signatures of Autism Using Machine Learning. Frontiers in Human Neuroscience 7.score: 90.0
    Alterations in neural connectivity have been suggested as a signature of the pathobiology of autism. Although disrupted correlation between cortical regions observed from functional MRI is considered to be an explanatory model for autism, the directional causal influence between brain regions is a vital link missing in these studies. The current study focuses on addressing this in an fMRI study of Theory-of-Mind in 15 high-functioning adolescents and adults with autism (ASD) and 15 typically developing (TD) controls. Participants viewed a (...)
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  9. Robert Shaw & James Todd (1980). Abstract Machine Theory and Direct Perception. Behavioral and Brain Sciences 3 (3):400.score: 90.0
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  10. 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.score: 84.0
    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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  11. Roy Harris (1987). The Language Machine. Cornell University Press.score: 78.0
  12. 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.score: 78.0
     
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  13. Malcolm R. Forster (1999). How Do Simple Rules `Fit to Reality' in a Complex World? Minds and Machines 9 (4):543-564.score: 74.0
    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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  14. 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.score: 74.0
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  15. 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.score: 72.0
    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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  16. 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.score: 72.0
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  17. Shalom Lappin with S. Shieber, Machine Learning Theory and Practice as a Source of Insight Into Universal Grammar.score: 72.0
  18. 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.score: 72.0
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  19. Abraham Robinson (1962). Review: A. Newell, J. C. Shaw, Programming the Logic Theory Machine. [REVIEW] Journal of Symbolic Logic 27 (1):103-103.score: 72.0
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  20. 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.score: 72.0
    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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  21. 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.score: 72.0
    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. 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.score: 72.0
    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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  23. 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.score: 72.0
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  24. 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.score: 72.0
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  25. 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.score: 72.0
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  26. 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.score: 72.0
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  27. 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.score: 72.0
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  28. 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.score: 72.0
     
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  29. 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.score: 72.0
     
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  30. 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.score: 72.0
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  31. C. E. M. Yates (1966). Review: Hartley Rogers, The Present Theory of Turing Machine Computability. [REVIEW] Journal of Symbolic Logic 31 (3):513-513.score: 72.0
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  32. Bernhard Schölkopf (2003). Statistical Learning Theory, Capacity, and Complexity. Complexity 8 (4):87-94.score: 66.0
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  33. Reza Zamani (2010). An Object-Oriented View on Problem Representation as a Search-Efficiency Facet: Minds Vs. Machines. [REVIEW] Minds and Machines 20 (1):103-117.score: 62.0
    From an object-oriented perspective, this paper investigates the interdisciplinary aspects of problem representation as well the differences between representation of problems in the mind and that in the machine. By defining an object as a combination of a symbol-structure and its associated operations, it shows how the representation of problems can become related to control, which conducts the search in finding a solution. Different types of representation of problems in the machine are classified into four categories, and in (...)
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  34. Arto Salomaa (1985). Computation and Automata. Cambridge University Press.score: 60.0
    This introduction to certain mathematical topics central to theoretical computer science treats computability and recursive functions, formal languages and automata, computational complexity, and cruptography. The presentation is essentially self-contained with detailed proofs of all statements provided. Although it begins with the basics, it proceeds to some of the most important recent developments in theoretical computer science.
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  35. Václav Pinkava (1988). Introduction to Logic for Systems Modelling. Abacus Press.score: 60.0
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  36. Gregory J. Chaitin (1970). Computational Complexity and Godel's Incompleteness Theorem. [Rio De Janeiro,Centro Técnico Científico, Pontifícia Universidade Católica Do Rio De Janeiro.score: 60.0
     
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  37. H. H. Rosenbrock (1990). Machines with a Purpose. Oxford University Press.score: 60.0
    There is at present a widespread unease about the direction in which our technology is taking us, apparently against our will. Promising advances seem to carry with them unforeseen negative consequences, including damage to the environment and the reduction of work to the trivial mechanical repetition of actions which have no human meaning. However, attempts to design a better, human-centered technology--one that complements rather than rejects human skills--are all too often frustrated by the prevailing belief that "man is a (...)," and one, moreover, that compares badly in terms of performance and durability. This contentious and stimulating book offers a new approach, one that refutes four centuries of science based on strictly causal explanations. It shows that man and nature can be viewed as "machines with a purpose," and that the "purpose" can be the advancement of technology to the benefit and not the detriment of the human race and its environment. This fascinating work is accessible to a wide range of readers, scientists and nonspecialists alike. It will interest anyone concerned about the impact of technology and the way it is shaping our world. (shrink)
     
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  38. Edgar Selzer (2011). Denn der Mensch Ist Mehr Als Sein Computer: Warum Die Turing-Maschine Das Wittgenstein'sche Sprachspiel Nicht Bewältigen Kann. Trauner.score: 60.0
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  39. Hao Wang (1962/1970). Logic, Computers, and Sets. New York,Chelsea Pub. Co..score: 60.0
     
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  40. Gerd Wechsung (ed.) (1984). Frege Conference 1984: Proceedings of the International Conference Held at Schwerin, Gdr, September 10-14, 1984. Akademie-Verlag.score: 60.0
  41. Michael J. Shaffer (2009). Decision Theory, Intelligent Planning and Counterfactuals. Minds and Machines 19 (1):61-92.score: 56.0
    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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  42. Shane Legg & Marcus Hutter (2007). Universal Intelligence: A Definition of Machine Intelligence. [REVIEW] Minds and Machines 17 (4):391-444.score: 56.0
    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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  43. Benjamin Wells (2002). Is There a Nonrecursive Decidable Equational Theory? Minds and Machines 12 (2):301-324.score: 56.0
    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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  44. Nigel Cutland (1980). Computability, an Introduction to Recursive Function Theory. Cambridge University Press.score: 54.0
    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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  45. 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.score: 54.0
    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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  46. Samuel Coskey & Joel David Hamkins (2010). Infinite Time Decidable Equivalence Relation Theory. Notre Dame Journal of Formal Logic 52 (2):203-228.score: 54.0
    We introduce an analogue of the theory of Borel equivalence relations in which we study equivalence relations that are decidable by an infinite time Turing machine. The Borel reductions are replaced by the more general class of infinite time computable functions. Many basic aspects of the classical theory remain intact, with the added bonus that it becomes sensible to study some special equivalence relations whose complexity is beyond Borel or even analytic. We also introduce an infinite time (...)
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  47. Natika Newton (1989). Machine Understanding and the Chinese Room. Philosophical Psychology 2 (2):207-15.score: 54.0
    John Searle has argued that one can imagine embodying a machine running any computer program without understanding the symbols, and hence that purely computational processes do not yield understanding. The disagreement this argument has generated stems, I hold, from ambiguity in talk of 'understanding'. The concept is analysed as a relation between subjects and symbols having two components: a formal and an intentional. The central question, then becomes whether a machine could possess the intentional component with or without (...)
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  48. Paul Bello & Selmer Bringsjord (2013). On How to Build a Moral Machine. Topoi 32 (2):251-266.score: 54.0
    Herein we make a plea to machine ethicists for the inclusion of constraints on their theories consistent with empirical data on human moral cognition. As philosophers, we clearly lack widely accepted solutions to issues regarding the existence of free will, the nature of persons and firm conditions on moral agency/patienthood; all of which are indispensable concepts to be deployed by any machine able to make moral judgments. No agreement seems forthcoming on these matters, and we don’t hold out (...)
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  49. Leonardo Badino, Alessandro D'Ausilio, Luciano Fadiga & Giorgio Metta (2014). Computational Validation of the Motor Contribution to Speech Perception. Topics in Cognitive Science 6 (3):461-475.score: 54.0
    Action perception and recognition are core abilities fundamental for human social interaction. A parieto-frontal network (the mirror neuron system) matches visually presented biological motion information onto observers' motor representations. This process of matching the actions of others onto our own sensorimotor repertoire is thought to be important for action recognition, providing a non-mediated “motor perception” based on a bidirectional flow of information along the mirror parieto-frontal circuits. State-of-the-art machine learning strategies for hand action identification have shown better performances when (...)
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  50. Valery M. Tsourikov (1993). Inventive Machine: Second Generation. [REVIEW] AI and Society 7 (1):62-77.score: 54.0
    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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