Search results for 'information and computation' (try it on Scholar)

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  1. Gordana Dodig Crnkovic & Mark Burgin (eds.) (forthcoming). INFORMATION AND COMPUTATION. World Scientific.score: 196.0
    The book focuses on relations between information and computation. Information is a basic structure of the world, while computation is a process of the dynamic change of information. In order for anything to exist for an individual, the individual must get information on it, either by means of perception or by re-organization of the existing information into new patterns and networks in the brain. With the advent of World Wide Web and a prospect (...)
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  2. Gualtiero Piccinini & Andrea Scarantino (2011). Information Processing, Computation, and Cognition. Journal of Biological Physics 37 (1):1-38.score: 192.0
    Computation and information processing are among the most fundamental notions in cognitive science. They are also among the most imprecisely discussed. Many cognitive scientists take it for granted that cognition involves computation, information processing, or both – although others disagree vehemently. Yet different cognitive scientists use ‘computation’ and ‘information processing’ to mean different things, sometimes without realizing that they do. In addition, computation and information processing are surrounded by several myths; first and (...)
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  3. Gualtiero Piccinini & Andrea Scarantino (2010). Computation Vs. Information Processing: Why Their Difference Matters to Cognitive Science. Studies in History and Philosophy of Science Part A 41 (3):237-246.score: 192.0
    Since the cognitive revolution, it’s 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 theoristError: Illegal (...)
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  4. Wesley Elsberry & Jeffrey Shallit (2011). Information Theory, Evolutionary Computation, and Dembski's "Complex Specified Information". Synthese 178 (2):237 - 270.score: 192.0
    Intelligent design advocate William Dembski has introduced a measure of information called "complex specified information", or CSI. He claims that CSI is a reliable marker of design by intelligent agents. He puts forth a "Law of Conservation of Information" which states that chance and natural laws are incapable of generating CSI. In particular, CSI cannot be generated by evolutionary computation. Dembski asserts that CSI is present in intelligent causes and in the flagellum of Escherichia coli, and (...)
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  5. Gordana Dodig Crnkovic & Susan Stuart (eds.) (2007). Computation, Information, Cognition: The Nexus and the Liminal. Cambridge Scholars Press.score: 192.0
    Written by world-leading experts, this book draws together a number of important strands in contemporary approaches to the philosophical and scientific questions that emerge when dealing with the issues of computing, information, cognition and the conceptual issues that arise at their intersections. It discovers and develops the connections at the borders and in the interstices of disciplines and debates. This volume presents a range of essays that deal with the currently vigorous concerns of the philosophy of information, ontology (...)
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  6. Nir Fresco (2013). Information Processing as an Account of Concrete Digital Computation. Philosophy and Technology 26 (1):31-60.score: 192.0
    It is common in cognitive science to equate computation (and in particular digital computation) with information processing. Yet, it is hard to find a comprehensive explicit account of concrete digital computation in information processing terms. An information processing account seems like a natural candidate to explain digital computation. But when ‘information’ comes under scrutiny, this account becomes a less obvious candidate. Four interpretations of information are examined here as the basis for (...)
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  7. Gordana Dodig-Crnkovic (2009). Information and Computation Nets. Investigations Into Info-Computational World. Vdm.score: 192.0
    The book presents investigations into the world of info-computational nature, in which information constitutes the structure, while computational process amounts to its change. Information and computation are inextricably bound: There is no computation without informational structure, and there is no information without computational process. Those two complementary ideas are used to build a conceptual net, which according to Novalis is a theoretical way of capturing reality. We apprehend the reality within a framework known as natural (...)
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  8. Nir Fresco & Marty J. Wolf (2014). The Instructional Information Processing Account of Digital Computation. Synthese 191 (7):1469-1492.score: 192.0
    What is nontrivial digital computation? It is the processing of discrete data through discrete state transitions in accordance with finite instructional information. The motivation for our account is that many previous attempts to answer this question are inadequate, and also that this account accords with the common intuition that digital computation is a type of information processing. We use the notion of reachability in a graph to defend this characterization in memory-based systems and underscore the importance (...)
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  9. Ken Herold (2014). Intuition, Computation, and Information. Minds and Machines 24 (1):85-88.score: 192.0
    Bynum (Putting information first: Luciano Floridi and the philosophy of information. NY: Wiley-Blackwell, 2010) identifies Floridi’s focus in the philosophy of information (PI) on entities both as data structures and as information objects. One suggestion for examining the association between the former and the latter stems from Floridi’s Herbert A. Simon Lecture in Computing and Philosophy given at Carnegie Mellon University in 2001, open problems in the PI: the transduction or transception, and how we gain knowledge (...)
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  10. M. J. Schroeder (2014). Information, Computation and Mind: Who Is in Charge of the Construction? Constructivist Foundations 9 (2):237-240.score: 174.0
    Open peer commentary on the article “Info-computational Constructivism and Cognition” by Gordana Dodig-Crnkovic. Upshot: Focusing on the relationship between info-computationalism and constructivism, I point out that there is a need to clarify fundamental concepts such as information, informational structures, and computation that obscure the theses regarding the relationship with constructivist thought. In particular, I wonder how we can reconcile constructivism with the view that all nature is a computational process.
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  11. Gordana Dodig-Crnkovic, Semantics of Information as Interactive Computation. Proceedings of the Fifth International Workshop on Philosophy and Informatics 2008.score: 168.0
    Computers today are not only the calculation tools - they are directly (inter)acting in the physical world which itself may be conceived of as the universal computer (Zuse, Fredkin, Wolfram, Chaitin, Lloyd). In expanding its domains from abstract logical symbol manipulation to physical embedded and networked devices, computing goes beyond Church-Turing limit (Copeland, Siegelman, Burgin, Schachter). Computational processes are distributed, reactive, interactive, agent-based and concurrent. The main criterion of success of computation is not its termination, but the adequacy of (...)
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  12. Michael Dickson (2007). Is Measurement a Black Box? On the Importance of Understanding Measurement Even in Quantum Information and Computation. Philosophy of Science 74 (5):1019–1032.score: 144.0
    It has been argued, partly from the lack of any widely accepted solution to the measurement problem, and partly from recent results from quantum information theory, that measurement in quantum theory is best treated as a black box. However, there is a crucial difference between ‘having no account of measurement' and ‘having no solution to the measurement problem'. We know a lot about measurements. Taking into account this knowledge sheds light on quantum theory as a theory of information (...)
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  13. John Collier, Information, Causation and Computation.score: 144.0
    Causation can be understood as a computational process once we understand causation in informational terms. I argue that if we see processes as information channels, then causal processes are most readily interpreted as the transfer of information from one state to another. This directly implies that the later state is a computation from the earlier state, given causal laws, which can also be interpreted computationally. This approach unifies the ideas of causation and computation.
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  14. Subhash C. Kak (1996). Information, Physics, and Computation. Foundations of Physics 26 (1):127-137.score: 144.0
    This paper presents several observations on the connections between information, physics, and computation. In particular, the computing power of quantum computers is examined. Quantum theory is characterized by superimposed states and nonlocal interactions. It is argued that recently studied quantum computers, which are based on local interactions, cannot simulate quantum physics.
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  15. Eric Dietrich & Arthur B. Markman (1998). All Information Processing Entails Computation, or, If R. A. Fisher Had Been a Cognitive Scientist . . Behavioral and Brain Sciences 21 (5):637-638.score: 132.0
    We argue that the dynamical and computational hypotheses are compatible and in fact need each other: they are about different aspects of cognition. However, only computationalism is about the information-processing aspect. We then argue that any form of information processing relying on matching and comparing, as cognition does, must use discrete representations and computations defined over them.
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  16. James V. Stone (1997). Information Theory: The Holy Grail of Cortical Computation? Behavioral and Brain Sciences 20 (4):698-698.score: 126.0
    Simple hypotheses are intrinsically attractive, and, for this reason, need to be formulated with utmost precision if they are to be testable. Unfortunately, it is hard to see how Phillips & Singer's hypothesis might be unambiguously refuted. Despite this, the authors have provided much evidence consistent with the hypothesis, and have proposed a natural and powerful extension for information theoretic approaches to learning.
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  17. Pieter Adriaans, Peter van Emde Boas & Fnwi Illc (2011). Computation, Information, and the Arrow of Time. In S. B. Cooper & Andrea Sorbi (eds.), Computability in Context: Computation and Logic in the Real World. World Scientific.score: 126.0
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  18. Jouko Vaananen (1997). Generalized Quantifiers and Computation, 9th European Summer School in Logic, Language, and Information, ESSLLI'97 Workshop, Aix-En-Provence, France, August 11-22, 1997, Revised Lectures. Springer.score: 124.0
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  19. Tim Fernando, Information and Computation.score: 122.0
    Situations serving as worlds as well as events in linguistic semantics are formulated as strings recording observations over discrete time. This formulation is applied to a linear temporal logic, in line with L. Schubert’s distinction between described and characterized situations. The distinction is developed topologically and computationally, and linked to the opposition between truth-conditional and proof-conditional semantics. For a finitary handle on quantification, strings are associated with situations not only on the basis of observation but also through derivation and constraint (...)
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  20. Varol Akman, Information-Oriented Computation With.score: 122.0
    While situation theory and situation semantics (Barwise and Perry 1983) provide an appropriate framework for a realistic model-theoretic treatment of natural language, serious thinking on their `computational' aspects has only recently started (Black 1993, Nakashima et al. 1988). Existing proposals mainly o er a Prolog- or Lisp-like programming environment with varying degrees of divergence from the ontology of situation theory. In this paper, we introduce a computational medium (called BABY-SIT) based on situations (T n and Akman 1994a, T n and (...)
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  21. Erkan Tin & Varol Akman (1996). Information-Oriented Computation with Baby-Sit. In Jerry Seligman & Dag Westerståhl (eds.), [Book Chapter]. Csli Publications, Stanford. 19--34.score: 122.0
    While situation theory and situation semantics provide an appropriate framework for a realistic model-theoretic treatment of natural language, serious thinking on their `computational' aspects has only recently started. Existing proposals mainly offer a Prolog- or Lisp-like programming environment with varying degrees of divergence from the ontology of situation theory. In this paper, we introduce a computational medium (called BABY-SIT) based on situations. The primary motivation underlying BABY-SIT is to facilitate the development and testing of programs in domains ranging from linguistics (...)
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  22. Jeffrey C. Alexander (1996). Conference on Information-Theoretic Approaches to Logic, Language, and Computation. History of the Human Sciences 18:21.score: 120.0
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  23. A. Duwell (2003). The Physics of Quantum Information: Quantum Cryptography, Quantum Teleportation, Quantum Computation - D. Bouwmeester, A. Ekert and A. Zeilinger (Eds.); Germany, 2000, 314pp, US$ 54, ISBN 3-540-66778-. [REVIEW] Studies in History and Philosophy of Science Part B 34 (2):331-334.score: 120.0
  24. M. J. (2001). On Bits and Quanta - Hoi-Kwong Lo, Sandu Popescu and Tim Spiller (Eds), Introduction to Quantum Computation and Information (Singapore: World Scientific, 1998), XI+348 Pp., ISBN 981-02-3399-X, £35, US$52. [REVIEW] Studies in History and Philosophy of Science Part B 32 (1):143-150.score: 120.0
  25. Stanley P. Gudder (2001). Book Review: Quantum Computation and Quantum Information. By Michael A. Nielsen and Isaac L. Chuang. Cambridge University Press, Cambridge, United Kingdom, 2000, I–Xxv+676 Pp., $42.00 (Hardcover). [REVIEW] Foundations of Physics 31 (11):1665-1667.score: 120.0
  26. Ruy J. G. B. de Queiroz (2004). 10th Workshop on Logic, Language, Information and Computation (WoLLIC '2003): Co-Sponsored by the Association for Symbolic Logic, Ouro Preto (Minas Gerais), Brazil July 29-August 1, 2003. [REVIEW] Bulletin of Symbolic Logic 10 (2):295-296.score: 120.0
  27. J. Andrew Brook & Robert J. Stainton, Fodor's New Theory of Computation and Information.score: 120.0
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  28. L. S. Baptista, A. Duran, T. Monteiro & A. G. de Oliveira (1996). 3rd Workshop on Logic, Language, Information and Computation (Wollic'96). Bulletin of Symbolic Logic 2 (3).score: 120.0
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  29. Ruy J. G. B. de Queiroz (2002). Eighth Workshop on Logic, Language, Information and Computation (WoLLIC '2001). Bulletin of Symbolic Logic 8 (2):319-320.score: 120.0
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  30. J. G. B. de Queiroz (2004). 10th Workshop on Logic, Language, Information and Computation (WoLLIC'2003). Bulletin of Symbolic Logic 10 (2):295-296.score: 120.0
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  31. Gordana Dodig-Crnkovic (2007). Epistemology as Computation (Information Processing). In Christian Calude (ed.), Randomness & Complexity, From Leibniz to Chaitin. World Scientific Pub Co Inc.score: 120.0
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  32. Erich Grädel (2003). 9th Workshop on Logic, Language, Information and Computation (Wollic'2002). Bulletin of Symbolic Logic 9 (1).score: 120.0
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  33. Luke Ong, Carlos Areces, Santiago Figueira & Ruy de Queiroz (forthcoming). 19th Workshop on Logic, Language, Information and Computation (Wollic 2012). Association for Symbolic Logic: The Bulletin of Symbolic Logic.score: 120.0
    Luke Ong, Carlos Areces, Santiago Figueira and Ruy de Queiroz The Bulletin of Symbolic Logic, Volume 19, Issue 3, Page 425-426, September 2013.
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  34. Lev Beklemishev, Ruy de Queiroz & Andre Scedrov (2012). 18th Workshop on Logic, Language, Information and Computation (Wollic 2011). Bulletin of Symbolic Logic 18 (1).score: 120.0
     
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  35. Alex Borgida & Alessandra Carbone (2008). 14th Workshop on Logic, Language, Information and Computation (Wollic 2007). Bulletin of Symbolic Logic 14 (1).score: 120.0
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  36. Anuj Dawar, Mauricio Ayala-Rincon & Ruy de Queiroz (2011). 17th Workshop on Logic, Language, Information and Computation (Wollic 2010). Bulletin of Symbolic Logic 17 (3):480-481.score: 120.0
     
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  37. Rio de Janeiro & Ruy J. G. B. de Queiroz (2003). 9th Workshop on Logic, Language, Information and Computation (WoLLIC'2002). Bulletin of Symbolic Logic 9 (1):121-122.score: 120.0
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  38. Ruy De Queiroz & Angus Macintyre (2008). Logic, Language, Information and Computation. Annals of Pure and Applied Logic 152 (1):1-2.score: 120.0
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  39. Ruy Jgb de Queiroz & C. A. Alto (1996). Second Workshop on Logic, Language, Information and Computation (WoLLIC'95). Logic Journal of the Igpl 4 (2):309-344.score: 120.0
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  40. Ruy J. G. B. de Queiroz, Angus Macintyre & Guilherme Bittencourt (eds.) (2005). 12th Workshop on Logic, Language, Information and Computation, Florianópolis, Brasil, 19 a 22 de Julho de 2005. [S.N.].score: 120.0
     
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  41. Ruy J. G. B. de Queiroz (2005). 11th Workshop on Logic, Language, Information and Computation (WoLLIC'2004). Bulletin of Symbolic Logic 11 (1):120-121.score: 120.0
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  42. Ruy J. G. B. de Queiroz (2005). 12th Workshop on Logic, Language, Information and Computation (WoLLIC'2005). Bulletin of Symbolic Logic 11 (4):558-559.score: 120.0
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  43. Ruy J. G. B. de Queiroz (2001). 7th Workshop on Logic, Language, Information and Computation (WoLLIC'2000). Bulletin of Symbolic Logic 7 (2):293-294.score: 120.0
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  44. Ruy J. G. B. de Queiroz (1999). 5th Workshop on Logic, Language, Information and Computation (Wollic'98). Bulletin of Symbolic Logic 5 (3):422-423.score: 120.0
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  45. Ruy J. G. B. de Queiroz & Walter Carnielli (1999). 6th Workshop on Logic, Language, Information and Computation (Wollic'99). Bulletin of Symbolic Logic 5 (3):424-425.score: 120.0
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  46. Alain Destexhe (1999). Books Etcetera-Biophysics of Computation: Information Processing in Single Neurons. Trends in Cognitive Sciences 3 (11):443.score: 120.0
     
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  47. Alain Destexhe (1999). Biophysics of Computation: Information Processing in Single Neurons, by Christof Koch. Trends in Cognitive Sciences 3 (11):444.score: 120.0
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  48. Sam Lomonaco & Makoto Kanazawa (2008). 15th Workshop on Logic, Language, Information and Computation (WoLLIC 2008)-Abstracts. Logic Journal of the Igpl 16 (4):415-423.score: 120.0
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  49. Carlos Areces Luke Ong (2013). 19th Workshop on Logic, Language, Information and Computation (Wollic 2012). Bulletin of Symbolic Logic 19 (3):425-426,.score: 120.0
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  50. Peter J. Marcer (1989). Quantum Computation: A Quantum Leap Towards Understanding Neural Information Processing. [REVIEW] AI and Society 3 (4):332-335.score: 120.0
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