Results for 'Computational Biomechanics'

971 found
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  1.  28
    Computing machines, body and mind: metaphorical origins of mechanistic computationalism.П. Н Барышников - 2023 - Philosophical Problems of IT and Cyberspace (PhilIT&C) 1:4-13.
    The article presents preliminary results of the conceptual analysis of the mechanistic profile of the computer metaphor. Mechanic reductionism is a special direction of computer metaphor rooted in various historical forms of word usage. Here we trace the stages of formation of the principles of transferring the properties of a mechanical computer to the properties of the human body and mind. We are also trying to identify the basic principles of semantic transfer, which have survived to this day in the (...)
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  2.  19
    Interdisciplinary character of the computational model in the solution of health problems.González Carbonell Raide Alfonso & Nápoles Padrón - 2014 - Humanidades Médicas 14 (3):646-658.
    La solución de los problemas de salud requiere de la interrelación entre diferentes ramas científicas y tecnológicas, tales como la Biomecánica, las tecnologías de la información, la ingeniería y las ciencias médicas. La importancia de la comunicación interdisciplinaria en las universidades fundamenta la creación de un grupo de investigación que fusiona el conocimiento de la mecánica clásica con las disciplinas de la salud y la participación de pacientes, técnicos, ingenieros, médicos y estudiantes. En el trabajo se realiza una reflexión sobre (...)
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  3.  21
    Characterizing Motor Control of Mastication With Soft Actor-Critic.Amir H. Abdi, Benedikt Sagl, Venkata P. Srungarapu, Ian Stavness, Eitan Prisman, Purang Abolmaesumi & Sidney Fels - 2020 - Frontiers in Human Neuroscience 14:523954.
    The human masticatory system is a complex functional unit characterized by a multitude of skeletal components, muscles, soft tissues, and teeth. Muscle activation dynamics cannot be directly measured on live human subjects due to ethical, safety, and accessibility limitations. Therefore, estimation of muscle activations and their resultant forces is a longstanding and active area of research. Reinforcement learning (RL) is an adaptive learning strategy which is inspired by the behavioral psychology and enables an agent to learn the dynamics of an (...)
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  4.  36
    The Fate of the Method of ‘Paradigms’ in Paleobiology.Martin J. S. Rudwick - 2018 - Journal of the History of Biology 51 (3):479-533.
    An earlier article described the mid-twentieth century origins of the method of “paradigms” in paleobiology, as a way of making testable hypotheses about the functional morphology of extinct organisms. The present article describes the use of “paradigms” through the 1970s and, briefly, to the end of the century. After I had proposed the paradigm method to help interpret the ecological history of brachiopods, my students developed it in relation to that and other invertebrate phyla, notably in Euan Clarkson’s analysis of (...)
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  5.  31
    Construction of 3D model of knee joint motion based on MRI image registration.Mohd Asif Shah, Zheng Wen Lai & Lei Zhang - 2021 - Journal of Intelligent Systems 31 (1):15-26.
    There is a growing demand for information and computational technology for surgeons help with surgical planning as well as prosthetics design. The two-dimensional images are registered to the three-dimensional (3D) model for high efficiency. To reconstruct the 3D model of knee joint including bone structure and main soft tissue structure, the evaluation and analysis of sports injury and rehabilitation treatment are detailed in this study. Mimics 10.0 was used to reconstruct the bone structure, ligament, and meniscus according to the (...)
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  6.  20
    Influence of the Location of a Decision Cue on the Dynamics of Pupillary Light Response.Pragya Pandey & Supriya Ray - 2022 - Frontiers in Human Neuroscience 15.
    The pupils of the eyes reflexively constrict in light and dilate in dark to optimize retinal illumination. Non-visual cognitive factors, like attention, arousal, decision-making, etc., also influence pupillary light response. During passive viewing, the eccentricity of a stimulus modulates the pupillary aperture size driven by spatially weighted corneal flux density, which is the product of luminance and the area of the stimulus. Whether the scope of attention also influences PLR remains unclear. In this study, we contrasted the pupil dynamics between (...)
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  7.  31
    Shaping mitotic chromosomes: From classical concepts to molecular mechanisms.Marc Kschonsak & Christian H. Haering - 2015 - Bioessays 37 (7):755-766.
    How eukaryotic genomes are packaged into compact cylindrical chromosomes in preparation for cell divisions has remained one of the major unsolved questions of cell biology. Novel approaches to study the topology of DNA helices inside the nuclei of intact cells, paired with computational modeling and precise biomechanical measurements of isolated chromosomes, have advanced our understanding of mitotic chromosome architecture. In this Review Essay, we discuss – in light of these recent insights – the role of chromatin architecture and the (...)
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  8.  30
    Allometry for the Twenty-First Century.Fred L. Bookstein - 2013 - Biological Theory 7 (1):10-25.
    The current literature that attempts to bridge between geometric morphometrics (GMM) and finite element analyses (FEA) of CT-derived data from bones of living animals and fossils appears to lack a sound biotheoretical foundation. To supply the missing rigor, the present article demonstrates a new rhetoric of quantitative inference across the GMM–FEA bridge—a rhetoric bridging form to function when both have been quantified so stringently. The suggested approach is founded on diverse standard textbook examples of the relation between forms and the (...)
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  9.  6
    A Model for Proustian Decay.Computer Lars - 2024 - Nordic Journal of Aesthetics 33 (67).
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  10. Extending Ourselves: Computational Science, Empiricism, and Scientific Method.Paul Humphreys - 2004 - New York, US: Oxford University Press.
    Computational methods such as computer simulations, Monte Carlo methods, and agent-based modeling have become the dominant techniques in many areas of science. Extending Ourselves contains the first systematic philosophical account of these new methods, and how they require a different approach to scientific method. Paul Humphreys draws a parallel between the ways in which such computational methods have enhanced our abilities to mathematically model the world, and the more familiar ways in which scientific instruments have expanded our access (...)
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  11. Randomness and Recursive Enumerability.Siam J. Comput - unknown
    One recursively enumerable real α dominates another one β if there are nondecreasing recursive sequences of rational numbers (a[n] : n ∈ ω) approximating α and (b[n] : n ∈ ω) approximating β and a positive constant C such that for all n, C(α − a[n]) ≥ (β − b[n]). See [R. M. Solovay, Draft of a Paper (or Series of Papers) on Chaitin’s Work, manuscript, IBM Thomas J. Watson Research Center, Yorktown Heights, NY, 1974, p. 215] and [G. J. (...)
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  12. Section 2. Model Theory.Va Vardanyan, On Provability Resembling Computability, Proving Aa Voronkov & Constructive Logic - 1989 - In Jens Erik Fenstad, Ivan Timofeevich Frolov & Risto Hilpinen (eds.), Logic, methodology, and philosophy of science VIII: proceedings of the Eighth International Congress of Logic, Methodology, and Philosophy of Science, Moscow, 1987. New York, NY, U.S.A.: Sole distributors for the U.S.A. and Canada, Elsevier Science.
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  13.  29
    Computational Philosophy of Science.Paul Thagard - 1988 - MIT Press.
    By applying research in artificial intelligence to problems in the philosophy of science, Paul Thagard develops an exciting new approach to the study of scientific reasoning. This approach uses computational ideas to shed light on how scientific theories are discovered, evaluated, and used in explanations. Thagard describes a detailed computational model of problem solving and discovery that provides a conceptually rich yet rigorous alternative to accounts of scientific knowledge based on formal logic, and he uses it to illuminate (...)
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  14. Computational Approaches to Concepts Representation: A Whirlwind Tour.Mattia Fumagalli, Riccardo Baratella, Marcello Frixione & Daniele Porello - forthcoming - Acta Analytica:1-32.
    The modelling of concepts, besides involving disciplines like philosophy of mind and psychology, is a fundamental and lively research problem in several artificial intelligence (AI) areas, such as knowledge representation, machine learning, and natural language processing. In this scenario, the most prominent proposed solutions adopt different (often incompatible) assumptions about the nature of such a notion. Each of these solutions has been developed to capture some specific features of concepts and support some specific (artificial) cognitive operations. This paper critically reviews (...)
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  15.  10
    Computer Science Logic: 11th International Workshop, CSL'97, Annual Conference of the EACSL, Aarhus, Denmark, August 23-29, 1997, Selected Papers.M. Nielsen, Wolfgang Thomas & European Association for Computer Science Logic - 1998 - Springer Verlag.
    This book constitutes the strictly refereed post-workshop proceedings of the 11th International Workshop on Computer Science Logic, CSL '97, held as the 1997 Annual Conference of the European Association on Computer Science Logic, EACSL, in Aarhus, Denmark, in August 1997. The volume presents 26 revised full papers selected after two rounds of refereeing from initially 92 submissions; also included are four invited papers. The book addresses all current aspects of computer science logics and its applications and thus presents the state (...)
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  16.  95
    Universality, Invariance, and the Foundations of Computational Complexity in the light of the Quantum Computer.Michael Cuffaro - 2018 - In Sven Ove Hansson (ed.), Technology and Mathematics: Philosophical and Historical Investigations. Cham, Switzerland: Springer Verlag. pp. 253-282.
    Computational complexity theory is a branch of computer science dedicated to classifying computational problems in terms of their difficulty. While computability theory tells us what we can compute in principle, complexity theory informs us regarding our practical limits. In this chapter I argue that the science of \emph{quantum computing} illuminates complexity theory by emphasising that its fundamental concepts are not model-independent, but that this does not, as some suggest, force us to radically revise the foundations of the theory. (...)
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  17.  36
    Computational Imagery.Janice Glasgow & Dimitri Papadias - 1992 - Cognitive Science 16 (3):355-394.
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  18. Computational semantics—linguistics and processing.John Nerbonne - 1996 - In Shalom Lappin (ed.), The handbook of contemporary semantic theory. Cambridge, Mass., USA: Blackwell Reference. pp. 459--82.
     
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  19.  40
    Computational Artifacts: Towards a Philosophy of Computer Science.Raymond Turner - 2018 - Springer Berlin Heidelberg.
    The philosophy of computer science is concerned with issues that arise from reflection upon the nature and practice of the discipline of computer science. This book presents an approach to the subject that is centered upon the notion of computational artefact. It provides an analysis of the things of computer science as technical artefacts. Seeing them in this way enables the application of the analytical tools and concepts from the philosophy of technology to the technical artefacts of computer science. (...)
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  20. Computational Number Theory.C. Pomerance - 2008 - In T. Gowers (ed.), Princeton Companion to Mathematics. Princeton University Press. pp. 348--362.
     
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  21. The computational and the representational language-of-thought hypotheses.David J. Chalmers - 2023 - Behavioral and Brain Sciences 46:e269.
    There are two versions of the language-of-thought hypothesis (LOT): Representational LOT (roughly, structured representation), introduced by Ockham, and computational LOT (roughly, symbolic computation) introduced by Fodor. Like many others, I oppose the latter but not the former. Quilty-Dunn et al. defend representational LOT, but they do not defend the strong computational LOT thesis central to the classical-connectionist debate.
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  22. Theoretical status of computational cognitive modeling.Ron Sun - unknown
    This article explores the view that computational models of cognition may constitute valid theories of cognition, often in the full sense of the term ‘‘theory”. In this discussion, this article examines various (existent or possible) positions on this issue and argues in favor of the view above. It also connects this issue with a number of other relevant issues, such as the general relationship between theory and data, the validation of models, and the practical benefits of computational modeling. (...)
     
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  23.  35
    A computational perspective on dissociating hippocampal and entorhinal function.Mark A. Gluck, Catherine E. Myers & James K. Goebel - 1994 - Behavioral and Brain Sciences 17 (3):476-477.
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  24. A computational study of lexical acquisition.Jeffrey Mark Siskind - 1995 - Cognition 50:1-33.
     
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  25. A Computational Cognitive Model of Syntactic Priming.David Reitter, Frank Keller & Johanna D. Moore - 2011 - Cognitive Science 35 (4):587-637.
    The psycholinguistic literature has identified two syntactic adaptation effects in language production: rapidly decaying short-term priming and long-lasting adaptation. To explain both effects, we present an ACT-R model of syntactic priming based on a wide-coverage, lexicalized syntactic theory that explains priming as facilitation of lexical access. In this model, two well-established ACT-R mechanisms, base-level learning and spreading activation, account for long-term adaptation and short-term priming, respectively. Our model simulates incremental language production and in a series of modeling studies, we show (...)
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  26. Explanation in Computational Psychology: Language, Perception and Level 1.5.Christopher Peacocke - 1986 - Mind and Language 1 (2):101-123.
  27.  34
    Computational research on interaction and agency.Philip E. Agre - 1995 - Artificial Intelligence 72 (1-2):1-52.
  28.  85
    Computational Rationality: Linking Mechanism and Behavior Through Bounded Utility Maximization.Richard L. Lewis, Andrew Howes & Satinder Singh - 2014 - Topics in Cognitive Science 6 (2):279-311.
    We propose a framework for including information‐processing bounds in rational analyses. It is an application of bounded optimality (Russell & Subramanian, 1995) to the challenges of developing theories of mechanism and behavior. The framework is based on the idea that behaviors are generated by cognitive mechanisms that are adapted to the structure of not only the environment but also the mind and brain itself. We call the framework computational rationality to emphasize the incorporation of computational mechanism into the (...)
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  29.  36
    A computational model to investigate assumptions in the headturn preference procedure.Christina Bergmann, Louis ten Bosch, Paula Fikkert & Lou Boves - 2013 - Frontiers in Psychology 4.
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  30.  24
    Reasoning about properties: A computational theory.Sangeet Khemlani & P. N. Johnson-Laird - 2022 - Psychological Review 129 (2):289-312.
  31. Human Enhancement and the Computational Metaphor.James Ogilvy - 2011 - Journal of Evolution and Technology 22 (1):81-96.
    This paper affirms human enhancement in principle, but questions the inordinate attention paid to two particular forms of enhancement: life extension and raising IQ. The argument is not about whether these enhancements are possible or not; instead, I question the aspirations behind the denial of death and the stress on one particular type of intelligence: the logico-analytic. Death is a form of finitude, and finitude is a crucially defining part of human life. As for intelligence, Howard Gardner and Daniel Goleman (...)
     
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  32.  23
    Modeling Misretrieval and Feature Substitution in Agreement Attraction: A Computational Evaluation.Dario Paape, Serine Avetisyan, Sol Lago & Shravan Vasishth - 2021 - Cognitive Science 45 (8):e13019.
    We present computational modeling results based on a self‐paced reading study investigating number attraction effects in Eastern Armenian. We implement three novel computational models of agreement attraction in a Bayesian framework and compare their predictive fit to the data using k‐fold cross‐validation. We find that our data are better accounted for by an encoding‐based model of agreement attraction, compared to a retrieval‐based model. A novel methodological contribution of our study is the use of comprehension questions with open‐ended responses, (...)
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  33.  24
    Computational Social Choice, 3–5 September.Ulle Endriss - 2008 - The Reasoner 2 (10):9-10.
  34. Is semantics computational?Mark Steedman & Matthew Stone - unknown
    Both formal semantics and cognitive semantics are the source of important insights about language. By developing precise statements of the rules of meaning in fragmentary, abstract languages, formalists have been able to offer perspicuous accounts of how we might come to know such rules and use them to communicate with others. Conversely, by charting the overall landscape of interpretations, cognitivists have documented how closely interpretations draw on the commonsense knowledge that lets us make our way in the world. There is (...)
     
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  35. Computational psychiatry.P. Read Montague, Raymond J. Dolan, Karl J. Friston & Peter Dayan - 2012 - Trends in Cognitive Sciences 16 (1):72-80.
  36. Explanation and description in computational neuroscience.David Michael Kaplan - 2011 - Synthese 183 (3):339-373.
    The central aim of this paper is to shed light on the nature of explanation in computational neuroscience. I argue that computational models in this domain possess explanatory force to the extent that they describe the mechanisms responsible for producing a given phenomenon—paralleling how other mechanistic models explain. Conceiving computational explanation as a species of mechanistic explanation affords an important distinction between computational models that play genuine explanatory roles and those that merely provide accurate descriptions or (...)
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  37.  49
    Active Inference as a Computational Framework for Consciousness.Martina G. Vilas, Ryszard Auksztulewicz & Lucia Melloni - 2022 - Review of Philosophy and Psychology 13 (4):859-878.
    Recently, the mechanistic framework of active inference has been put forward as a principled foundation to develop an overarching theory of consciousness which would help address conceptual disparities in the field (Wiese 2018 ; Hohwy and Seth 2020 ). For that promise to bear out, we argue that current proposals resting on the active inference scheme need refinement to become a process theory of consciousness. One way of improving a theory in mechanistic terms is to use formalisms such as (...) models that implement, attune and validate the conceptual notions put forward. Here, we examine how computational modelling approaches have been used to refine the theoretical proposals linking active inference and consciousness, with a focus on the extent and success to which they have been developed to accommodate different facets of consciousness and experimental paradigms, as well as how simulations and empirical data have been used to test and improve these computational models. While current attempts using this approach have shown promising results, we argue they remain preliminary in nature. To refine their predictive and structural validity, testing those models against empirical data is needed i.e., new and unobserved neural data. A remaining challenge for active inference to become a theory of consciousness is to generalize the model to accommodate the broad range of consciousness explananda; and in particular to account for the phenomenological aspects of experience. Notwithstanding these gaps, this approach has proven to be a valuable avenue for theory advancement and holds great potential for future research. (shrink)
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  38. (1 other version)Computational models.Paul Humphreys - 2002 - Proceedings of the Philosophy of Science Association 2002 (3):S1-S11.
    A different way of thinking about how the sciences are organized is suggested by the use of cross‐disciplinary computational methods as the organizing unit of science, here called computational templates. The structure of computational models is articulated using the concepts of construction assumptions and correction sets. The existence of these features indicates that certain conventionalist views are incorrect, in particular it suggests that computational models come with an interpretation that cannot be removed as well as a (...)
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  39.  44
    Computational Models of Emotion Inference in Theory of Mind: A Review and Roadmap.Desmond C. Ong, Jamil Zaki & Noah D. Goodman - 2019 - Topics in Cognitive Science 11 (2):338-357.
    An important, but relatively neglected, aspect of human theory of mind is emotion inference: understanding how and why a person feels a certain why is central to reasoning about their beliefs, desires and plans. The authors review recent work that has begun to unveil the structure and determinants of emotion inference, organizing them within a unified probabilistic framework.
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  40. The fortieth annual lecture series 1999-2000.Brain Computations & an Inevitable Conflict - 2000 - Journal for General Philosophy of Science / Zeitschrift für Allgemeine Wissenschaftstheorie 31:199-200.
  41. Computational Models of Performance Monitoring and Cognitive Control.William H. Alexander & Joshua W. Brown - 2010 - Topics in Cognitive Science 2 (4):658-677.
    The medial prefrontal cortex (mPFC) has been the subject of intense interest as a locus of cognitive control. Several computational models have been proposed to account for a range of effects, including error detection, conflict monitoring, error likelihood prediction, and numerous other effects observed with single-unit neurophysiology, fMRI, and lesion studies. Here, we review the state of computational models of cognitive control and offer a new theoretical synthesis of the mPFC as signaling response–outcome predictions. This new synthesis has (...)
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  42.  29
    The computational/representational paradigm as normal science: further support.Steven W. Zucker - 1980 - Behavioral and Brain Sciences 3 (3):406-407.
  43.  23
    Harnessing Computational Complexity Theory to Model Human Decision‐making and Cognition.Juan Pablo Franco & Carsten Murawski - 2023 - Cognitive Science 47 (6):e13304.
    A central aim of cognitive science is to understand the fundamental mechanisms that enable humans to navigate and make sense of complex environments. In this letter, we argue that computational complexity theory, a foundational framework for evaluating computational resource requirements, holds significant potential in addressing this challenge. As humans possess limited cognitive resources for processing vast amounts of information, understanding how humans perform complex cognitive tasks requires comprehending the underlying factors that drive information processing demands. Computational complexity (...)
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  44.  17
    Autism and Coherence: A Computational Model.Claire O.&Rsquolaughlin & Paul Thagard - 2000 - Mind and Language 15 (4):375-392.
    Recent theorizing about the nature of the cognitive impairment in autism suggests that autistic individuals display abnormally weak central coherence, the capacity to integrate information in order to make sense of one’s environment. Our article shows the relevance of computational models of coherence to the understanding of weak central coherence. Using a theory of coherence as constraint satisfaction, we show how weak coherence can be simulated ina a connectionist network that has unusually high inhibition compared to excitation. This connectionist (...)
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  45. Computational Correlates of Consciousness.Axel Cleeremans - 1963 - In Steven Laureys (ed.), The Boundaries of Consciousness: Neurobiology and Neuropathology: Progress in Brain Research. Elsevier.
    Over the past few years numerous proposals have appeared that attempt to characterize consciousness in terms of what could be called its computational correlates: Principles of information processing with which to characterize the differences between conscious and unconscious processing. Proposed computational correlates include architectural specialization (such as the involvement of specific regions of the brain in conscious processing), properties of representations (such as their stability in time or their strength), and properties of specific processes (such as resonance, synchrony, (...)
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  46.  40
    The computational complexity of hybrid temporal logics.C. Areces, P. Blackburn & M. Marx - 2000 - Logic Journal of the IGPL 8 (5):653-679.
    In their simplest form, hybrid languages are propositional modal languages which can refer to states. They were introduced by Arthur Prior, the inventor of tense logic, and played an important role in his work: because they make reference to specific times possible, they remove the most serious obstacle to developing modal approaches to temporal representation and reasoning. However very little is known about the computational complexity of hybrid temporal logics.In this paper we analyze the complexity of the satisfiability problem (...)
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  47.  13
    Computational and experimental observations of welds in third-generation nickel-based superalloys.Homam Naffakh-Moosavy - 2016 - Philosophical Magazine 96 (5):427-446.
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  48.  36
    The Computational Model of Linguistic Theory.Frank Nuessel - 1998 - Semiotics:128-139.
  49.  27
    A computational model of argumentation schemes for multi-agent systems.Alison R. Panisson, Peter McBurney & Rafael H. Bordini - 2021 - Argument and Computation 12 (3):357-395.
    There are many benefits of using argumentation-based techniques in multi-agent systems, as clearly shown in the literature. Such benefits come not only from the expressiveness that argumentation-based techniques bring to agent communication but also from the reasoning and decision-making capabilities under conditions of conflicting and uncertain information that argumentation enables for autonomous agents. When developing multi-agent applications in which argumentation will be used to improve agent communication and reasoning, argumentation schemes are useful in addressing the requirements of the application domain (...)
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  50. Computational model of Font-consistency and change effects on letter identification.T. Sanocki - 1989 - Bulletin of the Psychonomic Society 27 (6):523-523.
     
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