Results for 'computing'

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  1. 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.
  2. 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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  3.  9
    A Model for Proustian Decay.Computer Lars - 2024 - Nordic Journal of Aesthetics 33 (67).
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  4.  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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  5. Paul M. kjeldergaard.Pittsburgh Computations Centers - 1968 - In T. Dixon & Deryck Horton, Verbal Behavior and General Behavior Theory. Prentice-Hall.
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  6. 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, 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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  7.  22
    Hector freytes, Antonio ledda, Giuseppe sergioli and.Roberto Giuntini & Probabilistic Logics in Quantum Computation - 2013 - In Hanne Andersen, Dennis Dieks, Wenceslao J. Gonzalez, Thomas Uebel & Gregory Wheeler, New Challenges to Philosophy of Science. Springer Verlag. pp. 49.
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  8. The general problem of the primitive was finally solved in 1912 by A. Den-joy. But his integration process was more complicated than that of Lebesgue. Denjoy's basic idea was to first calculate the definite integral∫ b. [REVIEW]How to Compute Antiderivatives - 1995 - Bulletin of Symbolic Logic 1 (3).
     
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  9.  80
    (1 other version)Computer Ethics.Deborah G. Johnson - 2003 - In Luciano Floridi, The Blackwell guide to the philosophy of computing and information. Blackwell. pp. 63–75.
    The prelims comprise: Introduction Metatheoretical and Methodological Issues Applied and Synthetic Ethics Traditional and Emerging Issues Conclusion Websites and Other Resources.
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  10.  4
    Intentional identity revisited.Ahti Pietarinen A. School of Cognitive, Computing Sciences, Falmer, Brighton, BN1 9QH & Uk - 2010 - Nordic Journal of Philosophical Logic 6 (2):147-188.
    The problem of intentional identity, as originally offered by Peter Geach, says that there can be an anaphoric link between an indefinite term and a pronoun across a sentential boundary and across propositional attitude contexts, where the actual existence of an individual for the indefinite term is not presupposed. In this paper, a semantic resolution to this elusive puzzle is suggested, based on a new quantified intensional logic and game-theoretic semantics (GTS) of imperfect information. This constellation leads to an expressive (...)
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  11.  7
    A logical formalisation of false belief tasks.R. Velázquez-Quesada A. Institute for Logic Anthia Solaki Fernando, Computation Language, Netherlandsb Netherlands Organization for Applied Scientific Research, Media Studies Netherlandsc Information Science & Norway - forthcoming - Journal of Applied Non-Classical Logics:1-51.
    Theory of Mind (ToM), the cognitive capacity to attribute internal mental states to oneself and others, is a crucial component of social skills. Its formal study has become important, witness recent research on reasoning and information update by intelligent agents, and some proposals for its formal modelling have put forward settings based on Epistemic Logic (EL). Still, due to intrinsic idealisations, it is questionable whether EL can be used to model the high-order cognition of ‘real’ agents. This manuscript proposes a (...)
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  12. Cognitive and Computational Complexity: Considerations from Mathematical Problem Solving.Markus Pantsar - 2019 - Erkenntnis 86 (4):961-997.
    Following Marr’s famous three-level distinction between explanations in cognitive science, it is often accepted that focus on modeling cognitive tasks should be on the computational level rather than the algorithmic level. When it comes to mathematical problem solving, this approach suggests that the complexity of the task of solving a problem can be characterized by the computational complexity of that problem. In this paper, I argue that human cognizers use heuristic and didactic tools and thus engage in cognitive processes that (...)
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  13.  78
    The computer revolution and the problem of global ethics.Professor Krystyna Gorniak-Kocikowska - 1996 - Science and Engineering Ethics 2 (2):177-190.
    The author agrees with James Moor that computer technology, because it is ‘logically malleable’, is bringing about a genuine social revolution. Moor compares the computer revolution to the ‘industrial revolution’ of the late 18th and the 19th centuries; but it is argued here that a better comparison is with the ‘printing press revolution’ that occurred two centuries before that. Just as the major ethical theories of Bentham and Kant were developed in response to the printing press revolution, so a new (...)
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  14.  12
    Computing and Philosophy in Asia.Soraj Hongladarom (ed.) - 2007 - Cambridge Scholars Press.
    This volume is a collection of selected papers presented at the Second Asia-Pacific Computing and Philsosophy Conference, which was held in Bangkok, Thailand in January 2005. The conference was organized by the Center for Ethics of Science and Technology, Chulalongkorn University on behalf of the International Association of Computing and Philosophy (www.ia-cap.org). Computing have had a long relationship with philosophy, starting from the problem of how symbols being manipulated in computing bear a relation to the outside (...)
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  15.  45
    Computability and Complexity: From a Programming Perspective Vol. 21.N. D. Jones - 1997 - MIT Press.
    This makes his book especially valuable." -- Yuri Gurevich, Professor of Computer Science, University of Michigan Computability and complexity theory should be of central concern to practitioners as well as theorists.
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  16.  11
    Proceedings of the 1986 Conference on Theoretical Aspects of Reasoning about Knowledge: March 19-22, 1988, Monterey, California.Joseph Y. Halpern, International Business Machines Corporation, American Association of Artificial Intelligence, United States & Association for Computing Machinery - 1986
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  17. Syntax, Semantics, and Computer Programs.William J. Rapaport - 2020 - Philosophy and Technology 33 (2):309-321.
    Turner argues that computer programs must have purposes, that implementation is not a kind of semantics, and that computers might need to understand what they do. I respectfully disagree: Computer programs need not have purposes, implementation is a kind of semantic interpretation, and neither human computers nor computing machines need to understand what they do.
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  18.  63
    Computer Modeling in Philosophy of Religion.F. LeRon Shults - 2019 - Open Philosophy 2 (1):108-125.
    How might philosophy of religion be impacted by developments in computational modeling and social simulation? After briefly describing some of the content and context biases that have shaped traditional philosophy of religion, this article provides examples of computational models that illustrate the explanatory power of conceptually clear and empirically validated causal architectures informed by the bio-cultural sciences. It also outlines some of the material implications of these developments for broader metaphysical and metaethical discussions in philosophy. Computer modeling and simulation can (...)
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  19.  91
    Does Computation Reveal Machine Cognition?Prakash Mondal - 2014 - Biosemiotics 7 (1):97-110.
    This paper seeks to understand machine cognition. The nature of machine cognition has been shrouded in incomprehensibility. We have often encountered familiar arguments in cognitive science that human cognition is still faintly understood. This paper will argue that machine cognition is far less understood than even human cognition despite the fact that a lot about computer architecture and computational operations is known. Even if there have been putative claims about the transparency of the notion of machine computations, these claims do (...)
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  20. Computing the Extended Synthesis: Mapping the Dynamics and Conceptual Structure of the Evolvability Research Front.Laura Nuño de la Rosa - 2017 - Journal of Experimental Zoology Part B: Molecular and Developmental Evolution 328 (5):395–411.
    Since the late 1970s, the field of evolutionary biology has undergone empirical and theoretical developments that have threaten the pillars of evolutionary theory. Some evolutionary biologists have recently argued that evolutionary biology is not experiencing a paradigm shift, but an expansion of the modern synthesis. Philosophers of biology focusing on scientific practices seem to agree with this pluralistic interpretation and have argued that evolutionary theory should rather be seen as an organized network of multiple problem agendas with diverse disciplinary contributors. (...)
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  21.  10
    Computer Science Logic 5th Workshop, Csl '91, Berne, Switzerland, October 7-11, 1991 : Proceedings'.Egon Börger, Gerhard Jäger, Hans Kleine Büning & Michael M. Richter - 1992 - Springer Verlag.
    This volume presents the proceedings of the workshop CSL '91 (Computer Science Logic) held at the University of Berne, Switzerland, October 7-11, 1991. This was the fifth in a series of annual workshops on computer sciencelogic (the first four are recorded in LNCS volumes 329, 385, 440, and 533). The volume contains 33 invited and selected papers on a variety of logical topics in computer science, including abstract datatypes, bounded theories, complexity results, cut elimination, denotational semantics, infinitary queries, Kleene algebra (...)
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  22.  32
    Quantum Computation and Quantum Information.Michael A. Nielsen & Isaac L. Chuang - 2000 - Cambridge University Press.
    First-ever comprehensive introduction to the major new subject of quantum computing and quantum information.
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  23.  59
    Physical Computation: A Mechanistic Account.Gualtiero Piccinini - 2015 - Oxford, GB: Oxford University Press UK.
    Gualtiero Piccinini articulates and defends a mechanistic account of concrete, or physical, computation. A physical system is a computing system just in case it is a mechanism one of whose functions is to manipulate vehicles based solely on differences between different portions of the vehicles according to a rule defined over the vehicles. Physical Computation discusses previous accounts of computation and argues that the mechanistic account is better. Many kinds of computation are explicated, such as digital vs. analog, serial (...)
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  24. Computability and Logic.George Boolos, John Burgess, Richard P. & C. Jeffrey - 1980 - New York: Cambridge University Press. Edited by John P. Burgess & Richard C. Jeffrey.
    Computability and Logic has become a classic because of its accessibility to students without a mathematical background and because it covers not simply the staple topics of an intermediate logic course, such as Godel's incompleteness theorems, but also a large number of optional topics, from Turing's theory of computability to Ramsey's theorem. This 2007 fifth edition has been thoroughly revised by John Burgess. Including a selection of exercises, adjusted for this edition, at the end of each chapter, it offers a (...)
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  25.  30
    Computability of Self‐Similar Sets.Hiroyasu Kamo & Kiko Kawamura - 1999 - Mathematical Logic Quarterly 45 (1):23-30.
    We investigate computability of a self-similar set on a Euclidean space. A nonempty compact subset of a Euclidean space is called a self-similar set if it equals to the union of the images of itself by some set of contractions. The main result in this paper is that if all of the contractions are computable, then the self-similar set is a recursive compact set. A further result on the case that the self-similar set forms a curve is also discussed.
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  26. A computational foundation for the study of cognition.David Chalmers - 2011 - Journal of Cognitive Science 12 (4):323-357.
    Computation is central to the foundations of modern cognitive science, but its role is controversial. Questions about computation abound: What is it for a physical system to implement a computation? Is computation sufficient for thought? What is the role of computation in a theory of cognition? What is the relation between different sorts of computational theory, such as connectionism and symbolic computation? In this paper I develop a systematic framework that addresses all of these questions. Justifying the role of computation (...)
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  27. Why Simpler Computer Simulation Models Can Be Epistemically Better for Informing Decisions.Casey Helgeson, Vivek Srikrishnan, Klaus Keller & Nancy Tuana - 2021 - Philosophy of Science 88 (2):213-233.
    For computer simulation models to usefully inform climate risk management, uncertainties in model projections must be explored and characterized. Because doing so requires running the model many times over, and because computing resources are finite, uncertainty assessment is more feasible using models that demand less computer processor time. Such models are generally simpler in the sense of being more idealized, or less realistic. So modelers face a trade-off between realism and uncertainty quantification. Seeing this trade-off for the important epistemic (...)
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  28. Computing mechanisms.Gualtiero Piccinini - 2007 - Philosophy of Science 74 (4):501-526.
    This paper offers an account of what it is for a physical system to be a computing mechanism—a system that performs computations. A computing mechanism is a mechanism whose function is to generate output strings from input strings and (possibly) internal states, in accordance with a general rule that applies to all relevant strings and depends on the input strings and (possibly) internal states for its application. This account is motivated by reasons endogenous to the philosophy of (...), namely, doing justice to the practices of computer scientists and computability theorists. It is also an application of recent literature on mechanisms, because it assimilates computational explanation to mechanistic explanation. The account can be used to individuate computing mechanisms and the functions they compute and to taxonomize computing mechanisms based on their computing power. (shrink)
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  29. Computation and cognition: Issues in the foundation of cognitive science.Zenon W. Pylyshyn - 1980 - Behavioral and Brain Sciences 3 (1):111-32.
    The computational view of mind rests on certain intuitions regarding the fundamental similarity between computation and cognition. We examine some of these intuitions and suggest that they derive from the fact that computers and human organisms are both physical systems whose behavior is correctly described as being governed by rules acting on symbolic representations. Some of the implications of this view are discussed. It is suggested that a fundamental hypothesis of this approach is that there is a natural domain of (...)
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  30.  23
    A Computational Evaluation of Two Models of Retrieval Processes in Sentence Processing in Aphasia.Paula Lissón, Dorothea Pregla, Bruno Nicenboim, Dario Paape, Mick L. het Nederend, Frank Burchert, Nicole Stadie, David Caplan & Shravan Vasishth - 2021 - Cognitive Science 45 (4):e12956.
    Can sentence comprehension impairments in aphasia be explained by difficulties arising from dependency completion processes in parsing? Two distinct models of dependency completion difficulty are investigated, the Lewis and Vasishth (2005) activation‐based model and the direct‐access model (DA; McElree, 2000). These models' predictive performance is compared using data from individuals with aphasia (IWAs) and control participants. The data are from a self‐paced listening task involving subject and object relative clauses. The relative predictive performance of the models is evaluated using k‐fold (...)
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  31.  33
    Computing pagerank using power extrapolation.Christopher Manning - manuscript
    We present a novel technique for speeding up the computation of PageRank, a hyperlink-based estimate of the “importance” of Web pages, based on the ideas presented in [7]. The original PageRank algorithm uses the Power Method to compute successive iterates that converge to the principal eigenvector of the Markov matrix representing the Web link graph. The algorithm presented here, called Power Extrapolation, accelerates the convergence of the Power Method by subtracting off the error along several nonprincipal eigenvectors from the current (...)
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  32.  53
    Emergence, Computation and the Freedom Degree Loss Information Principle in Complex Systems.Ignazio Licata & Gianfranco Minati - 2017 - Foundations of Science 22 (4):863-881.
    We consider processes of emergence within the conceptual framework of the Information Loss principle and the concepts of systems conserving information; systems compressing information; and systems amplifying information. We deal with the supposed incompatibility between emergence and computability tout-court. We distinguish between computational emergence, when computation acquires properties, and emergent computation, when computation emerges as a property. The focus is on emergence processes occurring within computational processes. Violations of Turing-computability such as non-explicitness and incompleteness are intended to represent partially the (...)
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  33.  30
    Computational History of Philosophy of Science Dataset.Daniel J. Hicks, Rick Morris & Evelyn Brister - unknown
    The Computational History of Philosophy of Science Dataset aims to be a comprehensive set of article and book chapter metadata for philosophy of science. The dataset covers the full run of over 40 journals and 3 major book series in the field. An automated author disambiguation script is used to construct canonical names for each author, and a combination of gender attribution methods is used to attribute the gender of each author. The full code used to generate the dataset is (...)
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  34. (1 other version)Computing machinery and intelligence.Alan Turing - 1950 - Mind 59 (236):433-60.
    I propose to consider the question, "Can machines think?" This should begin with definitions of the meaning of the terms "machine" and "think." The definitions might be framed so as to reflect so far as possible the normal use of the words, but this attitude is dangerous, If the meaning of the words "machine" and "think" are to be found by examining how they are commonly used it is difficult to escape the conclusion that the meaning and the answer to (...)
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  35. 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 to the (...)
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  36. Computation as Involving Content: A Response to Egan.Christopher Peacocke - 1999 - Mind and Language 14 (2):195-202.
    Only computational explanations of a content‐involving sort can answer certain ‘how’‐questions; can support content‐involving counterfactuals; and have the generality characteristic of psychological explanations. Purely formal characteriza‐tions of computations have none of these properties, and do not determine content. These points apply not only to psychological explanation, but to Turing machines themselves. Computational explanations which involve content are not opposed to naturalism. They are also required if we are to explain the content‐involving properties of mental states.
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  37.  32
    A Computational-Hermeneutic Approach for Conceptual Explicitation.Christoph Benzmüller & David Fuenmayor - 2019 - In Matthieu Fontaine, Cristina Barés-Gómez, Francisco Salguero-Lamillar, Lorenzo Magnani & Ángel Nepomuceno-Fernández, Model-Based Reasoning in Science and Technology: Inferential Models for Logic, Language, Cognition and Computation. Springer Verlag.
    We present a computer-supported approach for the logical analysis and conceptual explicitation of argumentative discourse. Computational hermeneutics harnesses recent progresses in automated reasoning for higher-order logics and aims at formalizing natural-language argumentative discourse using flexible combinations of expressive non-classical logics. In doing so, it allows us to render explicit the tacit conceptualizations implicit in argumentative discursive practices. Our approach operates on networks of structured arguments and is iterative and two-layered. At one layer we search for logically correct formalizations for each (...)
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  38. Computers and computation in cognitive science.Tim van Gelder - 1998 - In T.M. Michalewicz, Advances in Computational Life Sciences Vol.2: Humans to Proteins. Melbourne: CSIRO Publishing.
    Digital computers play a special role in cognitive science—they may actually be instances of the phenomenon they are being used to model. This paper surveys some of the main issues involved in understanding the relationship between digital computers and cognition. It sketches the role of digital computers within orthodox computational cognitive science, in the light of a recently emerging alternative approach based around dynamical systems.
     
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  39. Computation vs. information processing: why their difference matters to cognitive science.Gualtiero Piccinini & Andrea Scarantino - 2010 - Studies in History and Philosophy of Science Part A 41 (3):237-246.
    Since the cognitive revolution, it has become commonplace that cognition involves both computation and information processing. Is this one claim or two? Is computation the same as information processing? The two terms are often used interchangeably, but this usage masks important differences. In this paper, we distinguish information processing from computation and examine some of their mutual relations, shedding light on the role each can play in a theory of cognition. We recommend that theorists of cognition be explicit and careful (...)
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  40. Computer metaphors for consciousness.Puran K. Bair - 1981 - In The Metaphors Of Consciousness. New York: Plenum Press.
     
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  41. Computer Simulations in Science and Engineering. Concept, Practices, Perspectives.Juan Manuel Durán - 2018 - Springer.
    This book addresses key conceptual issues relating to the modern scientific and engineering use of computer simulations. It analyses a broad set of questions, from the nature of computer simulations to their epistemological power, including the many scientific, social and ethics implications of using computer simulations. The book is written in an easily accessible narrative, one that weaves together philosophical questions and scientific technicalities. It will thus appeal equally to all academic scientists, engineers, and researchers in industry interested in questions (...)
  42. Learning Computer Networks Using Intelligent Tutoring System.Mones M. Al-Hanjori, Mohammed Z. Shaath & Samy S. Abu Naser - 2017 - International Journal of Advanced Research and Development 2 (1).
    Intelligent Tutoring Systems (ITS) has a wide influence on the exchange rate, education, health, training, and educational programs. In this paper we describe an intelligent tutoring system that helps student study computer networks. The current ITS provides intelligent presentation of educational content appropriate for students, such as the degree of knowledge, the desired level of detail, assessment, student level, and familiarity with the subject. Our Intelligent tutoring system was developed using ITSB authoring tool for building ITS. A preliminary evaluation of (...)
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  43.  22
    Computer Simulations as Fiction in Science.Miloš Agatonović - 2022 - Balkan Journal of Philosophy 14 (1):5-10.
    The present paper intends to show that computer simulations used in science are akin to fiction. Starting from the problem of defining computer simulation, the paper discusses the uses and disadvantages of simulations in science. Computer simulations have a representational function, but they do not resemble the phenomena that they purport to represent. Computer simulations do not preserve the content of the models, input data, and theories from which they proceed, since the content is modified by computational processes. Because of (...)
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  44. The computational conception of mind in acting and reflecting: The interdisciplinary turn.Dana S. Scott - 1990 - In Philosophy. Norwell: Kluwer.
  45.  12
    , "Computers as modelers of climate," in the greatest inventions of the past.William Calvin - manuscript
    Computer simulations may allow us to understand the earth’s fickle climate and how it is affected by detours of the great ocean currents. These detours cause abrupt coolings -- the average global temperature can drop dramatically in just a few years, with droughts that set up El-Niño-like forest fires even in the tropics. While volcanic eruptions and Antarctic ice shelf collapses can also abruptly cool things, what we’re talking about here is a flip-flop: a few centuries later, there’s an equally (...)
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  46. (1 other version)Computability and Logic.G. S. Boolos & R. C. Jeffrey - 1977 - British Journal for the Philosophy of Science 28 (1):95-95.
     
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  47.  39
    Validating computational models: A critique of Anderson's indeterminacy of representation claim.Zenon W. Pylyshyn - 1979 - Psychological Review 86 (4):383-394.
  48. Computer Science & IT with/for Biology.Enrico Franconi - unknown
    This reader contains the extended abstracts of the seminars organised for the “Computer Science and IT with/for Biology” Seminar Series, held at the Faculty of Computer Science, Free University of Bozen-Bolzano, from October to December 2005. Slides of the presentations are available online at: www.inf.unibz.it/krdb/biology.
     
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  49.  16
    Computer Science Logic.Dirk van Dalen & Marc Bezem (eds.) - 1997 - Springer.
    The related fields of fractal image encoding and fractal image analysis have blossomed in recent years. This book, originating from a NATO Advanced Study Institute held in 1995, presents work by leading researchers. It is developing the subjects at an introductory level, but it also has some recent and exciting results in both fields. The book contains a thorough discussion of fractal image compression and decompression, including both continuous and discrete formulations, vector space and hierarchical methods, and algorithmic optimizations. The (...)
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  50. Neural Computation and the Computational Theory of Cognition.Gualtiero Piccinini & Sonya Bahar - 2013 - Cognitive Science 37 (3):453-488.
    We begin by distinguishing computationalism from a number of other theses that are sometimes conflated with it. We also distinguish between several important kinds of computation: computation in a generic sense, digital computation, and analog computation. Then, we defend a weak version of computationalism—neural processes are computations in the generic sense. After that, we reject on empirical grounds the common assimilation of neural computation to either analog or digital computation, concluding that neural computation is sui generis. Analog computation requires continuous (...)
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