Search results for 'Computational Complexity' (try it on Scholar)

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  1. Jakub Szymanik (2009). The Computational Complexity of Quantified Reciprocals. In Peter Bosch, David Gabelaia & Jérôme Lang (eds.), Lecture Notes on Artificial Intelligence 5422, Logic, Language, and Computation 7th International Tbilisi Symposium on Logic, Language, and Computation. Springer.score: 182.0
    We study the computational complexity of reciprocal sentences with quantified antecedents. We observe a computational dichotomy between different interpretations of reciprocity, and shed some light on the status of the so-called Strong Meaning Hypothesis.
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  2. Jakub Szymanik (2009). Quantifiers in TIME and SPACE. Computational Complexity of Generalized Quantifiers in Natural Language. Dissertation, University of Amsterdamscore: 180.0
    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 (...)
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  3. Theodor Leiber (1999). Deterministic Chaos and Computational Complexity: The Case of Methodological Complexity Reductions. [REVIEW] Journal for General Philosophy of Science 30 (1):87-101.score: 180.0
    Some problems rarely discussed in traditional philosophy of science are mentioned: The empirical sciences using mathematico-quantitative theoretical models are frequently confronted with several types of computational problems posing primarily methodological limitations on explanatory and prognostic matters. Such limitations may arise from the appearances of deterministic chaos and (too) high computational complexity in general. In many cases, however, scientists circumvent such limitations by utilizing reductional approximations or complexity reductions for intractable problem formulations, thus constructing new models (...)
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  4. Jakub Szymanik (2010). Computational Complexity of Polyadic Lifts of Generalized Quantifiers in Natural Language. Linguistics and Philosophy 33 (3):215-250.score: 180.0
    We study the computational complexity of polyadic quantifiers in natural language. This type of quantification is widely used in formal semantics to model the meaning of multi-quantifier sentences. First, we show that the standard constructions that turn simple determiners into complex quantifiers, namely Boolean operations, iteration, cumulation, and resumption, are tractable. Then, we provide an insight into branching operation yielding intractable natural language multi-quantifier expressions. Next, we focus on a linguistic case study. We use computational complexity (...)
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  5. Fabian Schlotterbeck & Oliver Bott (2013). Easy Solutions for a Hard Problem? The Computational Complexity of Reciprocals with Quantificational Antecedents. Journal of Logic, Language and Information 22 (4):363-390.score: 180.0
    We report two experiments which tested whether cognitive capacities are limited to those functions that are computationally tractable (PTIME-Cognition Hypothesis). In particular, we investigated the semantic processing of reciprocal sentences with generalized quantifiers, i.e., sentences of the form Q dots are directly connected to each other, where Q stands for a generalized quantifier, e.g. all or most. Sentences of this type are notoriously ambiguous and it has been claimed in the semantic literature that the logically strongest reading is preferred (Strongest (...)
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  6. Patrick Blackburn & Edith Spaan (1993). A Modal Perspective on the Computational Complexity of Attribute Value Grammar. Journal of Logic, Language and Information 2 (2):129-169.score: 174.0
    Many of the formalisms used in Attribute Value grammar are notational variants of languages of propositional modal logic, and testing whether two Attribute Value Structures unify amounts to testing for modal satisfiability. In this paper we put this observation to work. We study the complexity of the satisfiability problem for nine modal languages which mirror different aspects of AVS description formalisms, including the ability to express re-entrancy, the ability to express generalisations, and the ability to express recursive constraints. Two (...)
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  7. Marcin Mostowski & Jakub Szymanik (2007). Computational Complexity of Some Ramsey Quantifiers in Finite Models. Bulletin of Symbolic Logic 13:281--282.score: 168.0
    The problem of computational complexity of semantics for some natural language constructions – considered in [M. Mostowski, D. Wojtyniak 2004] – motivates an interest in complexity of Ramsey quantifiers in finite models. In general a sentence with a Ramsey quantifier R of the following form Rx, yH(x, y) is interpreted as ∃A(A is big relatively to the universe ∧A2 ⊆ H). In the paper cited the problem of the complexity of the Hintikka sentence is reduced to (...)
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  8. Jeanne Ferrante (1979). The Computational Complexity of Logical Theories. Springer-Verlag.score: 150.0
    This book asks not only how the study of white-collar crime can enrich our understanding of crime and justice more generally, but also how criminological ...
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  9. Pascal Michel (2007). Computational Complexity of Logical Theories of One Successor and Another Unary Function. Archive for Mathematical Logic 46 (2):123-148.score: 150.0
    The first-order logical theory Th $({\mathbb{N}},x + 1,F(x))$ is proved to be complete for the class ATIME-ALT $(2^{O(n)},O(n))$ when $F(x) = 2^{x}$ , and the same result holds for $F(x) = c^{x}, x^{c} (c \in {\mathbb{N}}, c \ge 2)$ , and F(x) = tower of x powers of two. The difficult part is the upper bound, which is obtained by using a bounded Ehrenfeucht–Fraïssé game.
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  10. Felix Brandt, Felix Fischer & Paul Harrenstein (2009). The Computational Complexity of Choice Sets. Mathematical Logic Quarterly 55 (4):444-459.score: 150.0
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  11. Gregory J. Chaitin (1970). Computational Complexity and Godel's Incompleteness Theorem. [Rio De Janeiro,Centro Técnico Científico, Pontifícia Universidade Católica Do Rio De Janeiro.score: 150.0
     
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  12. Klaus Weirauch (2003). Computational Complexity on Computable Metric Spaces. Mathematical Logic Quarterly 49 (1):3-21.score: 150.0
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  13. Jarmo Kontinen (2013). Coherence and Computational Complexity of Quantifier-Free Dependence Logic Formulas. Studia Logica 101 (2):267-291.score: 132.0
    We study the computational complexity of the model checking problem for quantifier-free dependence logic ${(\mathcal{D})}$ formulas. We characterize three thresholds in the complexity: logarithmic space (LOGSPACE), non-deterministic logarithmic space (NL) and non-deterministic polynomial time (NP).
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  14. Matthias Scheutz (2001). Computational Vs. Causal Complexity. Minds And Machines 11 (4):543-566.score: 126.0
    The main claim of this paper is that notions of implementation based on an isomorphic correspondence between physical and computational states are not tenable. Rather, ``implementation'' has to be based on the notion of ``bisimulation'' in order to be able to block unwanted implementation results and incorporate intuitions from computational practice. A formal definition of implementation is suggested, which satisfies theoretical and practical requirements and may also be used to make the functionalist notion of ``physical realization'' precise. The (...)
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  15. Adam Morton (2004). Epistemic Virtues, Metavirtues, and Computational Complexity. Noûs 38 (3):481–502.score: 120.0
    I argue that considerations about computational complexity show that all finite agents need characteristics like those that have been called epistemic virtues. The necessity of these virtues follows in part from the nonexistence of shortcuts, or efficient ways of finding shortcuts, to cognitively expensive routines. It follows that agents must possess the capacities – metavirtues –of developing in advance the cognitive virtues they will need when time and memory are at a premium.
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  16. Todd Wareham, Iris van Rooij & Moritz Müller (2008). Computational Complexity Analysis Can Help, but First We Need a Theory. Behavioral and Brain Sciences 31 (4):399-400.score: 120.0
    Leech et al. present a connectionist algorithm as a model of (the development) of analogizing, but they do not specify the algorithm's associated computational-level theory, nor its computational complexity. We argue that doing so may be essential for connectionist cognitive models to have full explanatory power and transparency, as well as for assessing their scalability to real-world input domains.
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  17. Professor Adam Morton (2004). Epistemic Virtues, Metavirtues, and Computational Complexity. Noûs 38 (3):481-502.score: 120.0
    I argue that considerations about computational complexity show that all finite agents need characteristics like those that have been called epistemic virtues. The necessity of these virtues follows in part from the nonexistence of shortcuts, or efficient ways of finding shortcuts, to cognitively expensive routines. It follows that agents must possess the capacities – metavirtues –of developing in advance the cognitive virtues they will need when time and memory are at a premium.
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  18. Harvey Friedman, Lecture Notes on Term Rewriting and Computational Complexity.score: 120.0
    The main powerful method for establishing termination of term rewriting systems was discovered by Nachum Dershowitz through the introduction of certain natural well founded orderings (lexicographic path orderings). This leads to natural decision problems which may be of the highest computational complexity of any decidable problems appearing in a natural established computer science context.
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  19. Aleksandar Ignjatović (1995). Delineating Classes of Computational Complexity Via Second Order Theories with Weak Set Existence Principles. I. Journal of Symbolic Logic 60 (1):103-121.score: 120.0
    Aleksandar Ignjatović. Delineating Classes of Computational Complexity via Second Order Theories with Weak Set Existence Principles (I).
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  20. Ian Pratt-Hartmann (2008). On the Computational Complexity of the Numerically Definite Syllogistic and Related Logics. Bulletin of Symbolic Logic 14 (1):1-28.score: 120.0
    The numerically definite syllogistic is the fragment of English obtained by extending the language of the classical syllogism with numerical quantifiers. The numerically definite relational syllogistic is the fragment of English obtained by extending the numerically definite syllogistic with predicates involving transitive verbs. This paper investigates the computational complexity of the satisfiability problem for these fragments. We show that the satisfiability problem (= finite satisfiability problem) for the numerically definite syllogistic is strongly NP-complete, and that the satisfiability problem (...)
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  21. Karl-Heinz Niggl (2000). The $Mu$-Measure as a Tool for Classifying Computational Complexity. Archive for Mathematical Logic 39 (7):515-539.score: 120.0
    Two simply typed term systems $\sf {PR}_1$ and $\sf {PR}_2$ are considered, both for representing algorithms computing primitive recursive functions. $\sf {PR}_1$ is based on primitive recursion, $\sf {PR}_2$ on recursion on notation. A purely syntactical method of determining the computational complexity of algorithms in $\sf {PR}_i$ , called $\mu$ -measure, is employed to uniformly integrate traditional results in subrecursion theory with resource-free characterisations of sub-elementary complexity classes. Extending the Schwichtenberg and Müller characterisation of the Grzegorczyk classes (...)
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  22. Todd Wareham, Iris van Rooij & Moritz Müller (2008). Computational Complexity Analysis Can Help, but First We Need a Theory. Behavioral and Brain Sciences 31 (4):399-400.score: 120.0
    Leech et al. present a connectionist algorithm as a model of (the development) of analogizing, but they do not specify the algorithm's associated computational-level theory, nor its computational complexity. We argue that doing so may be essential for connectionist cognitive models to have full explanatory power and transparency, as well as for assessing their scalability to real-world input domains.
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  23. E. Börger (1989). Computability, Complexity, Logic. New York, N.Y., U.S.A.Elsevier Science Pub. Co..score: 114.0
    The theme of this book is formed by a pair of concepts: the concept of formal language as carrier of the precise expression of meaning, facts and problems, and the concept of algorithm or calculus, i.e. a formally operating procedure for the solution of precisely described questions and problems. The book is a unified introduction to the modern theory of these concepts, to the way in which they developed first in mathematical logic and computability theory and later in automata theory, (...)
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  24. Alexander Clark & Shalom Lappin (2013). Complexity in Language Acquisition. Topics in Cognitive Science 5 (1):89-110.score: 108.0
    Learning theory has frequently been applied to language acquisition, but discussion has largely focused on information theoretic problems—in particular on the absence of direct negative evidence. Such arguments typically neglect the probabilistic nature of cognition and learning in general. We argue first that these arguments, and analyses based on them, suffer from a major flaw: they systematically conflate the hypothesis class and the learnable concept class. As a result, they do not allow one to draw significant conclusions about the learner. (...)
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  25. Peter Lohmann & Heribert Vollmer (2013). Complexity Results for Modal Dependence Logic. Studia Logica 101 (2):343-366.score: 108.0
    Modal dependence logic was introduced recently by Väänänen. It enhances the basic modal language by an operator = (). For propositional variables p 1, . . . , p n , = (p 1, . . . , p n-1, p n ) intuitively states that the value of p n is determined by those of p 1, . . . , p n-1. Sevenster (J. Logic and Computation, 2009) showed that satisfiability for modal dependence logic is complete for nondeterministic (...)
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  26. David Danks, Clark Glymour & Peter Spirtes (2003). The Computational and Experimental Complexity of Gene Perturbations for Regulatory Network Search. In W. H. Hsu, R. Joehanes & C. D. Page (eds.), Proceedings of IJCAI-2003 workshop on learning graphical models for computational genomics.score: 108.0
    Various algorithms have been proposed for learning (partial) genetic regulatory networks through systematic measurements of differential expression in wild type versus strains in which expression of specific genes has been suppressed or enhanced, as well as for determining the most informative next experiment in a sequence. While the behavior of these algorithms has been investigated for toy examples, the full computational complexity of the problem has not received sufficient attention. We show that finding the true regulatory network requires (...)
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  27. Emil Jeřábek (2007). Complexity of Admissible Rules. Archive for Mathematical Logic 46 (2):73-92.score: 108.0
    We investigate the computational complexity of deciding whether a given inference rule is admissible for some modal and superintuitionistic logics. We state a broad condition under which the admissibility problem is coNEXP-hard. We also show that admissibility in several well-known systems (including GL, S4, and IPC) is in coNE, thus obtaining a sharp complexity estimate for admissibility in these systems.
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  28. Toshio Suzuki (2000). Complexity of the -Query Tautologies in the Presence of a Generic Oracle. Notre Dame Journal of Formal Logic 41 (2):142-151.score: 108.0
    Extending techniques of Dowd and those of Poizat, we study computational complexity of in the case when is a generic oracle, where is a positive integer, and denotes the collection of all -query tautologies with respect to an oracle . We introduce the notion of ceiling-generic oracles, as a generalization of Dowd's notion of -generic oracles to arbitrary finitely testable arithmetical predicates. We study how existence of ceiling-generic oracles affects behavior of a generic oracle, by which we show (...)
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  29. Fuxiang Yu (2007). On the Complexity of the Pancake Problem. Mathematical Logic Quarterly 53 (4):532-546.score: 108.0
    We study the computational complexity of finding a line that bisects simultaneously two sets in the two-dimensional plane, called the pancake problem, using the oracle Turing machine model of Ko. We also study the basic problem of bisecting a set at a given direction. Our main results are: (1) The complexity of bisecting a nice (thick) polynomial-time approximable set at a given direction can be characterized by the counting class #P. (2) The complexity of bisecting simultaneously (...)
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  30. Paul Cilliers (1998). Complexity and Postmodernism: Understanding Complex Systems. Routledge.score: 102.0
    Complexity and Postmodernism explores the notion of complexity in the light of contemporary perspectives from philosophy and science. The book integrates insights from complexity and computational theory with the philosophical position of thinkers including Derrida and Lyotard. Paul Cilliers takes a critical stance towards the use of the analytical method as a tool to cope with complexity, and he rejects Searle's superficial contribution to the debate.
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  31. McGraw-Hill, Computational Complexity and Godel's Incompleteness Theorem.score: 102.0
    Given any simply consistent formal theory F of the state complexity L(S) of finite binary sequences S as computed by 3-tape-symbol Turing machines, there exists a natural number L(F ) such that L(S) > n is provable in F only if n < L(F ). On the other hand, almost all finite binary sequences S satisfy L(S) > L(F ). The proof resembles Berry’s..
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  32. Marcin Zajenkowski, Rafał Styła & Jakub Szymanik (2011). A Computational Approach to Quantifiers as an Explanation for Some Language Impairments in Schizophrenia. Journal of Communication Disorder 44:2011.score: 102.0
    We compared the processing of natural language quantifiers in a group of patients with schizophrenia and a healthy control group. In both groups, the difficulty of the quantifiers was consistent with computational predictions, and patients with schizophrenia took more time to solve the problems. However, they were significantly less accurate only with proportional quantifiers, like more than half. This can be explained by noting that, according to the complexity perspective, only proportional quantifiers require working memory engagement.
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  33. Nikolai Zarkevich (2006). Structural Database for Reducing Cost in Materials Design and Complexity of Multiscale Computations. Complexity 11 (4):36-42.score: 102.0
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  34. Oliver Bott, Fabian Schlotterbeck & Jakub Szymanik (forthcoming). Interpreting Tractable Versus Intractable Reciprocal Sentences. In Proceedings of the International Conference on Computational Semantics.score: 96.0
    In three experiments, we investigated the computational complexity of German reciprocal sentences with different quantificational antecedents. Building upon the tractable cognition thesis (van Rooij, 2008) and its application to the verification of quantifiers (Szymanik, 2010) we predicted complexity differences among these sentences. Reciprocals with all-antecedents are expected to preferably receive a strong interpretation (Dalrymple et al., 1998), but reciprocals with proportional or numerical quantifier antecedents should be interpreted weakly. Experiment 1, where participants completed pictures according to their (...)
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  35. Iris van Rooij, Johan Kwisthout, Mark Blokpoel, Jakub Szymanik, Todd Wareham & Ivan Toni (2011). Intentional Communication: Computationally Easy or Difficult? Frontiers in Human Neuroscience 5.score: 96.0
    Human intentional communication is marked by its flexibility and context sensitivity. Hypothesized brain mechanisms can provide convincing and complete explanations of the human capacity for intentional communication only insofar as they can match the computational power required for displaying that capacity. It is thus of importance for cognitive neuroscience to know how computationally complex intentional communication actually is. Though the subject of considerable debate, the computational complexity of communication remains so far unknown. In this paper we defend (...)
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  36. Klaus Mainzer (2004). Thinking in Complexity: The Computational Dynamics of Matter, Mind, and Mankind. Springer.score: 96.0
    Even beginners and young graduate students will have something to learn from this book." (Andre Hautot, Physicalia, Vol. 57 (3), 2005)"All-in-all, this highly ...
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  37. Barkley Rosser, Computational and Dynamic Complexity in Economics.score: 96.0
    This paper examines the rising competition between computational and dynamic conceptualizations of complexity in economics. While computable economics views the complexity as something rigorously defined based on concepts from probability, information, and computability criteria, dynamic complexity is based on whether a system endogenously and deterministically generates erratically dynamic behavior of certain kinds. On such behavior is the phenomenon of emergence, the appearance of new forms or structures at higher levels of a system from processes occurring at (...)
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  38. Arto Salomaa (1985). Computation and Automata. Cambridge University Press.score: 96.0
    This introduction to certain mathematical topics central to theoretical computer science treats computability and recursive functions, formal languages and automata, computational complexity, and cruptography. The presentation is essentially self-contained with detailed proofs of all statements provided. Although it begins with the basics, it proceeds to some of the most important recent developments in theoretical computer science.
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  39. Marco Cesati & Miriam Dilanni (1997). Computation Models for Parameterized Complexity. Mathematical Logic Quarterly 43 (2):179-202.score: 96.0
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  40. Sergei P. Odintsov & Stanislav O. Speranski (2013). Computability Issues for Adaptive Logics in Multi-Consequence Standard Format. Studia Logica 101 (6):1237-1262.score: 96.0
    In a rather general setting, we prove a number of basic theorems concerning computational complexity of derivability in adaptive logics. For that setting, the so-called standard format of adaptive logics is suitably adopted, and the corresponding completeness results are established in a very uniform way.
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  41. Martin Dyer & Leen Stougie (2005). Computational Complexity of Stochastic Programming Problems. Complexity 1 (13):21.score: 96.0
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  42. Guido Governatori, Francesco Olivieri, Antonino Rotolo & Simone Scannapieco (2013). Computing Strong and Weak Permissions in Defeasible Logic. Journal of Philosophical Logic 42 (6):799-829.score: 96.0
    In this paper we propose an extension of Defeasible Logic to represent and compute different concepts of defeasible permission. In particular, we discuss some types of explicit permissive norms that work as exceptions to opposite obligations or encode permissive rights. Moreover, we show how strong permissions can be represented both with, and without introducing a new consequence relation for inferring conclusions from explicit permissive norms. Finally, we illustrate how a preference operator applicable to contrary-to-duty obligations can be combined with a (...)
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  43. P. Oliva (2002). On the Computational Complexity of Best L~1-Approximation. Mathematical Logic Quarterly 48 (S1):66-77.score: 94.0
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  44. I. Pitowsky (1996). Laplace's Demon Consults an Oracle: The Computational Complexity of Prediction. Studies in History and Philosophy of Science Part B 27 (2):161-180.score: 90.0
  45. Christopher Cherniak (1984). Computational Complexity and the Universal Acceptance of Logic. Journal of Philosophy 81 (12):739-758.score: 90.0
  46. Marcello Frixione (2001). Tractable Competence. Minds and Machines 11 (3):379-397.score: 90.0
    In the study of cognitive processes, limitations on computational resources (computing time and memory space) are usually considered to be beyond the scope of a theory of competence, and to be exclusively relevant to the study of performance. Starting from considerations derived from the theory of computational complexity, in this paper I argue that there are good reasons for claiming that some aspects of resource limitations pertain to the domain of a theory of competence.
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  47. Robert I. Soare (1977). Computational Complexity, Speedable and Levelable Sets. Journal of Symbolic Logic 42 (4):545-563.score: 90.0
  48. Donald A. Alton (1976). Diversity of Speed-Ups and Embeddability in Computational Complexity. Journal of Symbolic Logic 41 (1):199-214.score: 90.0
  49. Barry E. Jacobs (1977). On Generalized Computational Complexity. Journal of Symbolic Logic 42 (1):47-58.score: 90.0
  50. Frank Wimberly, David Danks, Clark Glymour & Tianjiao Chu, Problems for Structure Learning: Aggregation and Computational Complexity.score: 90.0
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