Search results for 'Computer algorithms' (try it on Scholar)

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  1. A. P. Ershov & Donald Ervin Knuth (eds.) (1981). Algorithms in Modern Mathematics and Computer Science: Proceedings, Urgench, Uzbek Ssr, September 16-22, 1979. Springer-Verlag.score: 132.0
  2. Michael E. Cuffaro (forthcoming). How-Possibly Explanations in Quantum Computer Science. Philosophy of Science.score: 78.0
    A primary goal of quantum computer science is to find an explanation for the fact that quantum computers are more powerful than classical computers. In this paper I argue that to answer this question is to compare algorithmic processes of various kinds, and in so doing to describe the possibility spaces associated with these processes. By doing this we explain how it is possible for one process to outperform its rival. Further, in this and similar examples little is gained (...)
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  3. O. B. Lupanov (ed.) (2005). Stochastic Algorithms: Foundations and Applications: Third International Symposium, Saga 2005, Moscow, Russia, October 20-22, 2005: Proceedings. [REVIEW] Springer.score: 78.0
    This book constitutes the refereed proceedings of the Third International Symposium on Stochastic Algorithms: Foundations and Applications, SAGA 2005, held in Moscow, Russia in October 2005. The 14 revised full papers presented together with 5 invited papers were carefully reviewed and selected for inclusion in the book. The contributed papers included in this volume cover both theoretical as well as applied aspects of stochastic computations whith a special focus on new algorithmic ideas involving stochastic decisions and the design and (...)
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  4. Donald Ervin Knuth (2010). Selected Papers on Design of Algorithms. Center for the Study of Language and Information.score: 78.0
  5. William J. Rapaport (2005). Philosophy of Computer Science. Teaching Philosophy 28 (4):319-341.score: 76.0
    There are many branches of philosophy called “the philosophy of X,” where X = disciplines ranging from history to physics. The philosophy of artificial intelligence has a long history, and there are many courses and texts with that title. Surprisingly, the philosophy of computer science is not nearly as well-developed. This article proposes topics that might constitute the philosophy of computer science and describes a course covering those topics, along with suggested readings and assignments.
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  6. Jiri Becvar (1971). Review: Robert R. Korfhage, Logic and Algorithms with Applications to the Computer and Information Sciences. [REVIEW] Journal of Symbolic Logic 36 (2):344-346.score: 72.0
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  7. W. Dean (forthcoming). Algorithms and the Mathematical Foundations of Computer Science. Notre Dame Journal of Formal Logic.score: 72.0
     
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  8. Charles Rackoff (2006). Dietzfelbinger Martin. Primality Testing in Polynomial Time—From Randomized Algorithms to “PRIMES is in P”. Lecture Notes in Computer Science, Vol. 3000. Springer-Verlag, 2004, X+ 147 Pp. [REVIEW] Bulletin of Symbolic Logic 12 (3):494-496.score: 72.0
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  9. Carol E. Cleland (2001). Recipes, Algorithms, and Programs. Minds and Machines 11 (2):219-237.score: 66.0
    In the technical literature of computer science, the concept of an effective procedure is closely associated with the notion of an instruction that precisely specifies an action. Turing machine instructions are held up as providing paragons of instructions that "precisely describe" or "well define" the actions they prescribe. Numerical algorithms and computer programs are judged effective just insofar as they are thought to be translatable into Turing machine programs. Nontechnical procedures (e.g., recipes, methods) are summarily dismissed as (...)
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  10. Fahiem Bacchus & Toby Walsh (eds.) (2005). Theory and Applications of Satisfiability Testing: 8th International Conference, Sat 2005, St Andrews, Uk, June 19-23, 2005: Proceedings. [REVIEW] Springer.score: 66.0
    This book constitutes the refereed proceedings of the 8th International Conference on Theory and Applications of Satisfiability Testing, SAT 2005, held in St Andrews, Scotland in June 2005. The 26 revised full papers presented together with 16 revised short papers presented as posters during the technical programme were carefully selected from 73 submissions. The whole spectrum of research in propositional and quantified Boolean formula satisfiability testing is covered including proof systems, search techniques, probabilistic analysis of algorithms and their properties, (...)
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  11. Holger H. Hoos & David G. Mitchell (eds.) (2005). Theory and Applications of Satisfiability Testing: 7th International Conference, Sat 2004, Vancouver, Bc, Canada, May 10-13, 2004: Revised Selected Papers. [REVIEW] Springer.score: 66.0
    This book constitutes the refereed proceedings of the 7th International Conference on Theory and Applications of Satisfiability Testing, SAT 2004, held in Vancouver, BC, Canada in May 2004. The 24 revised full papers presented together with 2 invited papers were carefully selected from 72 submissions. In addition there are 2 reports on the 2004 SAT Solver Competition and the 2004 QBF Solver Evaluation. The whole spectrum of research in propositional and quantified Boolean formula satisfiability testing is covered; bringing together the (...)
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  12. Ofer Strichman & Stefan Szeider (eds.) (2010). Theory and Applications of Satisfiability Testing-- Sat 2010: 13th International Conference, Sat 2010 Edinburgh, Uk, July 2010: Proceedings. [REVIEW] Springer.score: 66.0
    The LNCS series reports state-of-the-art results in computer science research, development, and education, at a high level and in both printed and electronic form.
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  13. Richard Heersmink, Jeroen van den Hoven, Nees Jan van Eck & Jan van den Berg (2011). Bibliometric Mapping of Computer and Information Ethics. Ethics and Information Technology 13 (3):241-249.score: 60.0
    This paper presents the first bibliometric mapping analysis of the field of computer and information ethics (C&IE). It provides a map of the relations between 400 key terms in the field. This term map can be used to get an overview of concepts and topics in the field and to identify relations between information and communication technology concepts on the one hand and ethical concepts on the other hand. To produce the term map, a data set of over thousand (...)
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  14. David A. Nelson (1992). Deductive Program Verification (a Practitioner's Commentary). Minds and Machines 2 (3):283-307.score: 60.0
    A proof of ‘correctness’ for a mathematical algorithm cannot be relevant to executions of a program based on that algorithm because both the algorithm and the proof are based on assumptions that do not hold for computations carried out by real-world computers. Thus, proving the ‘correctness’ of an algorithm cannot establish the trustworthiness of programs based on that algorithm. Despite the (deceptive) sameness of the notations used to represent them, the transformation of an algorithm into an executable program is a (...)
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  15. Karem A. Sakallah & Laurent Simon (eds.) (2011). Theory and Application of Satisfiability Testing - Sat 2011: 14th International Conference, Sat 2011, Ann Arbor, Mi, Usa, June 19-22, 2011: Proceedings. [REVIEW] Springer.score: 60.0
    This book constitutes the refereed proceedings of the 14th International Conference on Theory and Applications of Satisfiability Testing, SAT 2011, held in Ann Arbor, MI, USA in June 2011.The 25 revised full papers presented together with ...
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  16. John Franco, Endre Boros & P. L. Hammer (eds.) (1999). The Satisfiability Problem. Elsevier.score: 60.0
  17. Timothy R. Colburn (1991). Program Verification, Defeasible Reasoning, and Two Views of Computer Science. Minds and Machines 1 (1):97-116.score: 54.0
    In this paper I attempt to cast the current program verification debate within a more general perspective on the methodologies and goals of computer science. I show, first, how any method involved in demonstrating the correctness of a physically executing computer program, whether by testing or formal verification, involves reasoning that is defeasible in nature. Then, through a delineation of the senses in which programs can be run as tests, I show that the activities of testing and formal (...)
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  18. Jeff Edmonds (2008). How to Think About Algorithms. Cambridge University Press.score: 54.0
    There are many algorithm texts that provide lots of well-polished code and proofs of correctness. Instead, this book presents insights, notations, and analogies to help the novice describe and think about algorithms like an expert. By looking at both the big picture and easy step-by-step methods for developing algorithms, the author helps students avoid the common pitfalls. He stresses paradigms such as loop invariants and recursion to unify a huge range of algorithms into a few meta-algorithms. (...)
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  19. Rolf Niedermeier (2006). Invitation to Fixed-Parameter Algorithms. Oxford University Press.score: 54.0
    A fixed-parameter is an algorithm that provides an optimal solution to a combinatorial problem. This research-level text is an application-oriented introduction to the growing and highly topical area of the development and analysis of efficient fixed-parameter algorithms for hard problems. The book is divided into three parts: a broad introduction that provides the general philosophy and motivation; followed by coverage of algorithmic methods developed over the years in fixed-parameter algorithmics forming the core of the book; and a discussion of (...)
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  20. M. W. Bunder & R. M. Rizkalla (2009). Proof-Finding Algorithms for Classical and Subclassical Propositional Logics. Notre Dame Journal of Formal Logic 50 (3):261-273.score: 54.0
    The formulas-as-types isomorphism tells us that every proof and theorem, in the intuitionistic implicational logic $H_\rightarrow$, corresponds to a lambda term or combinator and its type. The algorithms of Bunder very efficiently find a lambda term inhabitant, if any, of any given type of $H_\rightarrow$ and of many of its subsystems. In most cases the search procedure has a simple bound based roughly on the length of the formula involved. Computer implementations of some of these procedures were done (...)
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  21. Nicholas Furl, P. Jonathon Phillips & Alice J. O'Toole (2002). Face Recognition Algorithms and the Other‐Race Effect: Computational Mechanisms for a Developmental Contact Hypothesis. Cognitive Science 26 (6):797-815.score: 48.0
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  22. Lev Manovich (2000). Database as a Genre of New Media. AI and Society 14 (2):176-183.score: 48.0
    After the novel, and subsequently cinema privileged narrative as the key form of cultural expression of the modern age, the computer age introduces its correlate — database. Why does new media favour database form over others? Can we explain ist popularity by analysing the specificity of the digital medium and of computer programming? What is the relationship between database and another form, which has traditionally dominated human culture — narrative? In addressing these questions, I discuss the connection between (...)
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  23. Jakub Szymanik (2009). Quantifiers in TIME and SPACE. Computational Complexity of Generalized Quantifiers in Natural Language. Dissertation, University of Amsterdamscore: 46.0
    In the dissertation we study the complexity of generalized quantifiers in natural language. Our perspective is interdisciplinary: we combine philosophical insights with theoretical computer science, experimental cognitive science and linguistic theories. -/- In Chapter 1 we argue for identifying a part of meaning, the so-called referential meaning (model-checking), with algorithms. Moreover, we discuss the influence of computational complexity theory on cognitive tasks. We give some arguments to treat as cognitively tractable only those problems which can be computed in (...)
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  24. M. Ben-Ari (1993/2003). Mathematical Logic for Computer Science. Prentice Hall.score: 42.0
    Mathematical Logic for Computer Science is a mathematics textbook with theorems and proofs, but the choice of topics has been guided by the needs of computer science students. The method of semantic tableaux provides an elegant way to teach logic that is both theoretically sound and yet sufficiently elementary for undergraduates. To provide a balanced treatment of logic, tableaux are related to deductive proof systems.The logical systems presented are:- Propositional calculus (including binary decision diagrams);- Predicate calculus;- Resolution;- Hoare (...)
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  25. Robert L. Constable, The Triumph of Types: Principia Mathematica's Impact on Computer Science.score: 42.0
    Types now play an essential role in computer science; their ascent originates from Principia Mathematica. Type checking and type inference algorithms are used to prevent semantic errors in programs, and type theories are the native language of several major interactive theorem provers. Some of these trace key features back to Principia.
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  26. A. Feigenbaum Edward (1984). Computer-Assisted Decision Making in Medicine. Journal of Medicine and Philosophy 9 (2).score: 42.0
    This article reviews the strengths and limitations of five major paradigms of medical computer-assisted decision making (CADM): (1) clinical algorithms, (2) statistical analysis of collections of patient data, (3) mathematical models of physical processes, (4) decision analysis, and (5) symbolic reasoning or artificial intelligence (Al). No one technique is best for all applications, and there is recent promising work which combines two or more established techniques. We emphasize both the inherent power of symbolic reasoning and the promise of (...)
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  27. J. C. Kunz, E. H. Shortliffe, B. G. Buchanan & E. A. Feigenbaum (1984). Computer-Assisted Decision Making in Medicine. Journal of Medicine and Philosophy 9 (2):135-160.score: 42.0
    This article reviews the strengths and limitations of five major paradigms of medical computer-assisted decision making (CADM): (1) clinical algorithms, (2) statistical analysis of collections of patient data, (3) mathematical models of physical processes, (4) decision analysis, and (5) symbolic reasoning or artificial intelligence (Al). No one technique is best for all applications, and there is recent promising work which combines two or more established techniques. We emphasize both the inherent power of symbolic reasoning and the promise of (...)
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  28. Peter Wegner (1999). Towards Empirical Computer Science. The Monist 82 (1):58-108.score: 42.0
    Part I presents a model of interactive computation and a metric for expressiveness, Part II relates interactive models of computation to physics, and Part III considers empirical models from a philosophical perspective. Interaction machines, which extend Turing Machines to interaction, are shown in Part I to be more expressive than Turing Machines by a direct proof, by adapting Gödel's incompleteness result, and by observability metrics. Observation equivalence provides a tool for measuring expressiveness according to which interactive systems are more expressive (...)
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  29. Kiyoko F. Aoki-Kinoshita, Minoru Kanehisa, Ming-Yang Kao, Xiang-Yang Li & Weizhao Wang (2006). Session 2A-Approximation Algorithms-A 6-Approximation Algorithm for Computing Smallest Common AoN-Supertree with Application to the Reconstruction of Glycan Trees. [REVIEW] In O. Stock & M. Schaerf (eds.), Lecture Notes in Computer Science. Springer-Verlag. 100-110.score: 42.0
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  30. Boyana Norris (2006). Minisymposia-VIII Advanced Algorithms and Software Components for Scientific Computing-Software Architecture Issues in Scientific Component Development. In O. Stock & M. Schaerf (eds.), Lecture Notes in Computer Science. Springer-Verlag. 3732--629.score: 42.0
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  31. Jianfeng Shen, Chun Jin & Peng Gao (2006). Evolutionary Computation: Theory and Algorithms-A Nested Genetic Algorithm for Optimal Container Pick-Up Operation Scheduling on Container Yards. In O. Stock & M. Schaerf (eds.), Lecture Notes in Computer Science. Springer-Verlag. 4221--666.score: 42.0
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  32. Christian Weihrauch, Ivan Dimov, Simon Branford & Vassil Alexandrov (2006). Parallel Monte Carlo Algorithms for Diverse Applications in a Distributed Setting-Comparison of the Computational Cost of a Monte Carlo and Deterministic Algorithm for Computing Bilinear Forms Of. In O. Stock & M. Schaerf (eds.), Lecture Notes in Computer Science. Springer-Verlag. 640-647.score: 42.0
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  33. Patrick Saint-Dizier & Evelyne Viegas (eds.) (1995). Computational Lexical Semantics. Cambridge University Press.score: 40.0
    Lexical semantics has become a major research area within computational linguistics, drawing from psycholinguistics, knowledge representation, computer algorithms and architecture. Research programmes whose goal is the definition of large lexicons are asking what the appropriate representation structure is for different facets of lexical information. Among these facets, semantic information is probably the most complex and the least explored.Computational Lexical Semantics is one of the first volumes to provide models for the creation of various kinds of computerised lexicons for (...)
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  34. Eric Steinhart (2002). Logically Possible Machines. Minds and Machines 12 (2):259-280.score: 36.0
    I use modal logic and transfinite set-theory to define metaphysical foundations for a general theory of computation. A possible universe is a certain kind of situation; a situation is a set of facts. An algorithm is a certain kind of inductively defined property. A machine is a series of situations that instantiates an algorithm in a certain way. There are finite as well as transfinite algorithms and machines of any degree of complexity (e.g., Turing and super-Turing machines and more). (...)
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  35. Jeffrey Barrett (2004). Computer Implication and the Curry Paradox. Journal of Philosophical Logic 33 (6):631 - 637.score: 36.0
    There are theoretical limitations to what can be implemented by a computer program. In this paper we are concerned with a limitation on the strength of computer implemented deduction. We use a version of the Curry paradox to arrive at this limitation.
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  36. Wayne Aitken & Jeffrey A. Barrett (2004). Computer Implication and the Curry Paradox. Journal of Philosophical Logic 33 (6):631-637.score: 36.0
    There are theoretical limitations to what can be implemented by a computer program. In this paper we are concerned with a limitation on the strength of computer implemented deduction. We use a version of the Curry paradox to arrive at this limitation.
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  37. Robert T. Pennock (2000). Can Darwinian Mechanisms Make Novel Discoveries?: Learning From Discoveries Made by Evolving Neural Networks. [REVIEW] Foundations of Science 5 (2):225-238.score: 36.0
    Some philosophers suggest that the development of scientificknowledge is a kind of Darwinian process. The process of discovery,however, is one problematic element of this analogy. I compare HerbertSimon's attempt to simulate scientific discovery in a computer programto recent connectionist models that were not designed for that purpose,but which provide useful cases to help evaluate this aspect of theanalogy. In contrast to the classic A.I. approach Simon used, ``neuralnetworks'' contain no explicit protocols, but are generic learningsystems built on the model (...)
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  38. Normal D. Megill & Mladen Pavičić (2002). Deduction, Ordering, and Operations in Quantum Logic. Foundations of Physics 32 (3):357-378.score: 36.0
    We show that in quantum logic of closed subspaces of Hilbert space one cannot substitute quantum operations for classical (standard Hilbert space) ones and treat them as primitive operations. We consider two possible ways of such a substitution and arrive at operation algebras that are not lattices what proves the claim. We devise algorithms and programs which write down any two-variable expression in an orthomodular lattice by means of classical and quantum operations in an identical form. Our results show (...)
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  39. Carsten Seck (2012). Metaphysics Within Chemical Physics: The Case of Ab Initio Molecular Dynamics. [REVIEW] Journal for General Philosophy of Science 43 (2):361-375.score: 36.0
    This paper combines naturalized metaphysics and a philosophical reflection on a recently evolving interdisciplinary branch of quantum chemistry, ab initio molecular dynamics. Bridging the gaps among chemistry, physics, and computer science, this cutting-edge research field explores the structure and dynamics of complex molecular many-body systems through computer simulations. These simulations are allegedly crafted solely by the laws of fundamental physics, and are explicitly designed to capture nature as closely as possible. The models and algorithms employed, however, involve (...)
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  40. Edoardo Mollona & Andrea Marcozzi (2009). Self-Emerging Coordination Mechanisms for Knowledge Integration Processes. Mind and Society 8 (2):223-241.score: 36.0
    The increasing knowledge intensity of jobs, typical of a knowledge economy, highlights the role of firms as integrators of know-how and skills. As economic activity becomes mainly intellectual and requires the integration of specific and idiosyncratic skills, firms need to allocate skills to tasks and traditional hierarchical control results increasingly ineffective. In this work, we explore under what circumstances networks of agents, which bear specific skills, may self-organize in order to complete tasks. We use a computer simulation approach and (...)
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  41. Thomas Serre Sébastien M. Crouzet (2011). What Are the Visual Features Underlying Rapid Object Recognition? Frontiers in Psychology 2.score: 36.0
    Research progress in machine vision has been very significant in recent years. Robust face detection and identification algorithms are already readily available to consumers, and modern computer vision algorithms for generic object recognition are now coping with the richness and complexity of natural visual scenes. Unlike early vision models of object recognition that emphasized the role of figure-ground segmentation and spatial information between parts, recent successful approaches are based on the computation of loose collections of image features (...)
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  42. F. B. Cannonito (1970). Review: A. Wlodzimierz Mostowski, On the Decidability of Some Problems in Special Classes of Groups; A. Wlodzimierz Mostowski, Computational Algorithms for Deciding Some Problems for Nilpotent Groups. [REVIEW] Journal of Symbolic Logic 35 (3):476-477.score: 36.0
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  43. Marcin Miłkowski (2009). Is Evolution Algorithmic? Minds and Machines 19 (4):465-475.score: 34.0
    In Darwin’s Dangerous Idea, Daniel Dennett claims that evolution is algorithmic. On Dennett’s analysis, evolutionary processes are trivially algorithmic because he assumes that all natural processes are algorithmic. I will argue that there are more robust ways to understand algorithmic processes that make the claim that evolution is algorithmic empirical and not conceptual. While laws of nature can be seen as compression algorithms of information about the world, it does not follow logically that they are implemented as algorithms (...)
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  44. Robert F. Hadley (2008). Consistency, Turing Computability and Gödel's First Incompleteness Theorem. Minds and Machines 18 (1):1-15.score: 34.0
    It is well understood and appreciated that Gödel’s Incompleteness Theorems apply to sufficiently strong, formal deductive systems. In particular, the theorems apply to systems which are adequate for conventional number theory. Less well known is that there exist algorithms which can be applied to such a system to generate a gödel-sentence for that system. Although the generation of a sentence is not equivalent to proving its truth, the present paper argues that the existence of these algorithms, when conjoined (...)
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  45. Wayne Aitken & Jeffrey A. Barrett (2007). Stability and Paradox in Algorithmic Logic. Journal of Philosophical Logic 36 (1):61 - 95.score: 34.0
    There is significant interest in type-free systems that allow flexible self-application. Such systems are of interest in property theory, natural language semantics, the theory of truth, theoretical computer science, the theory of classes, and category theory. While there are a variety of proposed type-free systems, there is a particularly natural type-free system that we believe is prototypical: the logic of recursive algorithms. Algorithmic logic is the study of basic statements concerning algorithms and the algorithmic rules of inference (...)
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  46. Jeffrey Barrett (2007). Stability and Paradox in Algorithmic Logic. Journal of Philosophical Logic 36 (1):61 - 95.score: 34.0
    There is significant interest in type-free systems that allow flexible self-application. Such systems are of interest in property theory, natural language semantics, the theory of truth, theoretical computer science, the theory of classes, and category theory. While there are a variety of proposed type-free systems, there is a particularly natural type-free system that we believe is prototypical: the logic of recursive algorithms. Algorithmic logic is the study of basic statements concerning algorithms and the algorithmic rules of inference (...)
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  47. G. J. Chaitin, How to Run Algorithmic Information Theory on a Computer.score: 32.0
    Hi everybody! It's a great pleasure for me to be back here at the new, improved Santa Fe Institute in this spectacular location. I guess this is my fourth visit and it's always very stimulating, so I'm always very happy to visit you guys. I'd like to tell you what I've been up to lately. First of all, let me say what algorithmic information theory is good for, before telling you about the new version of it I've got.
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  48. David E. Goldberg, Kumara Sastry & Xavier Llorà (2007). Toward Routine Billion‐Variable Optimization Using Genetic Algorithms. Complexity 12 (3):27-29.score: 32.0
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  49. B. Jack Copeland (1996). What is Computation? Synthese 108 (3):335-59.score: 30.0
    To compute is to execute an algorithm. More precisely, to say that a device or organ computes is to say that there exists a modelling relationship of a certain kind between it and a formal specification of an algorithm and supporting architecture. The key issue is to delimit the phrase of a certain kind. I call this the problem of distinguishing between standard and nonstandard models of computation. The successful drawing of this distinction guards Turing's 1936 analysis of computation against (...)
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  50. Nachum Dershowitz & Yuri Gurevich (2008). A Natural Axiomatization of Computability and Proof of Church's Thesis. Bulletin of Symbolic Logic 14 (3):299-350.score: 30.0
    Church's Thesis asserts that the only numeric functions that can be calculated by effective means are the recursive ones, which are the same, extensionally, as the Turing-computable numeric functions. The Abstract State Machine Theorem states that every classical algorithm is behaviorally equivalent to an abstract state machine. This theorem presupposes three natural postulates about algorithmic computation. Here, we show that augmenting those postulates with an additional requirement regarding basic operations gives a natural axiomatization of computability and a proof of Church's (...)
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