Search results for 'algorithm' (try it on Scholar)

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  1. Kees van Deemter, Albert Gatt, Ielka van der Sluis & Richard Power (2011). Generation of Referring Expressions: Assessing the Incremental Algorithm. Cognitive Science 36 (5):799-836.score: 18.0
    A substantial amount of recent work in natural language generation has focused on the generation of ‘‘one-shot’’ referring expressions whose only aim is to identify a target referent. Dale and Reiter's Incremental Algorithm (IA) is often thought to be the best algorithm for maximizing the similarity to referring expressions produced by people. We test this hypothesis by eliciting referring expressions from human subjects and computing the similarity between the expressions elicited and the ones generated by algorithms. It turns (...)
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  2. Emiel Krahmer, Ruud Koolen & Mariët Theune (2012). Is It That Difficult to Find a Good Preference Order for the Incremental Algorithm? Cognitive Science 36 (5):837-841.score: 18.0
    In a recent article published in this journal (van Deemter, Gatt, van der Sluis, & Power, 2012), the authors criticize the Incremental Algorithm (a well-known algorithm for the generation of referring expressions due to Dale & Reiter, 1995, also in this journal) because of its strong reliance on a pre-determined, domain-dependent Preference Order. The authors argue that there are potentially many different Preference Orders that could be considered, while often no evidence is available to determine which is a (...)
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  3. Bruno Mesz, Mariano Sigman & Marcos Trevisan (2012). A Composition Algorithm Based on Crossmodal Taste-Music Correspondences. Frontiers in Human Neuroscience 6.score: 18.0
    While there is broad consensus about the structural similarities between language and music, comparably less attention has been devoted to semantic correspondences between these two ubiquitous manifestations of human culture. We have investigated the relations between music and a narrow and bounded domain of semantics: the words and concepts referring to taste sensations. In a recent work, we found that taste words were consistently mapped to musical parameters. Bitter is associated with low-pitched and continuous music (legato), salty is characterized by (...)
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  4. Kees van Deemter, Albert Gatt, Ielka van der Sluis & Richard Power (2012). Generation of Referring Expressions: Assessing the Incremental Algorithm. Cognitive Science 36 (5):799-836.score: 15.0
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  5. S. Felsenthal & Mosh� Machover (1992). Sequential Voting by Veto: Making the Mueller-Moulin Algorithm More Versatile. Theory and Decision 33 (3):223-240.score: 15.0
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  6. Susan Hanekon, Sue Berney, Brenda Morrow, George Ntoumenopoulos, Jennifer Paratz, Shane Patman & Quinette Louw (2011). The Validation of a Clinical Algorithm for the Prevention and Management of Pulmonary Dysfunction in Intubated Adults: A Synthesis of Evidence and Expert Opinion. Journal of Evaluation in Clinical Practice 17 (4):801-810.score: 15.0
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  7. Peter Juslin & Magnus Persson (2002). PROBabilities From EXemplars (PROBEX): A “Lazy” Algorithm for Probabilistic Inference From Generic Knowledge. Cognitive Science 26 (5):563-607.score: 15.0
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  8. Wolfgang Klimesch (2013). An Algorithm for the EEG Frequency Architecture of Consciousness and Brain Body Coupling. Frontiers in Human Neuroscience 7.score: 15.0
  9. Shuzhuo Li, Yinghui Chen, Haifeng Du & Marcus W. Feldman (2010). A Genetic Algorithm with Local Search Strategy for Improved Detection of Community Structure. Complexity 15 (4):53-60.score: 15.0
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  10. Matthias Müller-Hannemann & Stefan Schirra (eds.) (2010). Algorithm Engineering: Bridging the Gap Between Algorithm Theory and Practice. Springer.score: 14.0
    Driven by concrete applications, Algorithm Engineering complements theory by the benefits of experimentation and puts equal emphasis on all aspects arising during a cyclic solution process ranging from realistic modeling, design, analysis, ...
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  11. Elżbieta Hajnicz (1996). Applying Allen's Constraint Propagation Algorithm for Non-Linear Time. Journal of Logic, Language and Information 5 (2):157-175.score: 14.0
    The famous Allen's interval relations constraint propagation algorithm was intended for linear time. Its 13 primitive relations define all the possible mutual locations of two intervals on the time-axis. In this paper an application of the algorithm for non-linear time is suggested. First, a new primitive relation is added. It is called excludes since an occurrence of one event in a certain course of events excludes an occurrence of the other event in this course. Next, new composition rules (...)
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  12. Kees van Deemter, Albert Gatt, Ielka van der Sluis & Richard Power (2012). Assessing the Incremental Algorithm: A Response to Krahmer Et Al. Cognitive Science 36 (5):842-845.score: 14.0
    This response discusses the experiment reported in Krahmer et al.’s Letter to the Editor of Cognitive Science. We observe that their results do not tell us whether the Incremental Algorithm is better or worse than its competitors, and we speculate about implications for reference in complex domains, and for learning from ‘‘normal” (i.e., non-semantically-balanced) corpora.
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  13. Paola Cantu' & Italo Testa (2011). Algorithms and Arguments: The Foundational Role of the ATAI-Question. In Frans H. van Eemeren, Bart Garssen, David Godden & Gordon Mitchell (eds.), Proceedings of the Seventh International Conference of the International Society for the Study of Argumentation (pp. 192-203). Rozenberg / Sic Sat.score: 12.0
    Argumentation theory underwent a significant development in the Fifties and Sixties: its revival is usually connected to Perelman's criticism of formal logic and the development of informal logic. Interestingly enough it was during this period that Artificial Intelligence was developed, which defended the following thesis (from now on referred to as the AI-thesis): human reasoning can be emulated by machines. The paper suggests a reconstruction of the opposition between formal and informal logic as a move against a premise of an (...)
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  14. Peter Spirtes, An Anytime Algorithm for Causal Inference.score: 12.0
    The Fast Casual Inference (FCI) algorithm searches for features common to observationally equivalent sets of causal directed acyclic graphs. It is correct in the large sample limit with probability one even if there is a possibility of hidden variables and selection bias. In the worst case, the number of conditional independence tests performed by the algorithm grows exponentially with the number of variables in the data set. This affects both the speed of the algorithm and the accuracy (...)
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  15. Peter Spirtes, A Polynomial Time Algorithm for Determining Dag Equivalence in the Presence of Latent Variables and Selection Bias.score: 12.0
    if and only if for every W in V, W is independent of the set of all its non-descendants conditional on the set of its parents. One natural question that arises with respect to DAGs is when two DAGs are “statistically equivalent”. One interesting sense of “statistical equivalence” is “d-separation equivalence” (explained in more detail below.) In the case of DAGs, d-separation equivalence is also corresponds to a variety of other natural senses of statistical equivalence (such as representing the same (...)
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  16. Tim Holmes & Johannes M. Zanker (2013). Investigating Preferences for Colour-Shape Combinations with Gaze Driven Optimization Method Based on Evolutionary Algorithms. Frontiers in Psychology 4:926.score: 12.0
    Studying aesthetic preference is notoriously difficult because it targets individual experience. Eye movements provide a rich source of behavioural measures that directly reflect subjective choice. To determine individual preferences for simple composition rules we here use fixation duration as the fitness measure in a Gaze Driven Evolutionary Algorithm (GDEA), which has been used as a tool to identify aesthetic preferences (Holmes & Zanker, 2012). In the present study, the GDEA was used to investigate the preferred combination of colour and (...)
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  17. Varol Akman, A Simple and Efficient Haloed Line Algorithm for Hidden Line Elimination.score: 12.0
    An efficient algorithm, HALO, is given to compute As computer aided design (CAD) deals with more com- haloed line drawings of wire frame objects. (Haloed..
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  18. Itamar Pitowsky, The Number of Elements in a Subset: A Grover-Kronecker Quantum Algorithm.score: 12.0
    In a fundamental paper [Phys. Rev. Lett. 78, 325 (1997)] Grover showed how a quantum computer can …nd a single marked object in a database of size N by using only O(pN ) queries of the oracle that identi…es the object. His result was generalized to the case of …nding one object in a subset of marked elements. We consider the following computational problem: A subset of marked elements is given whose number of elements is either M or K, M (...)
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  19. Martin Peterson (2011). Is There an Ethics of Algorithms? Ethics and Information Technology 13 (3):251-260.score: 12.0
    We argue that some algorithms are value-laden, and that two or more persons who accept different value-judgments may have a rational reason to design such algorithms differently. We exemplify our claim by discussing a set of algorithms used in medical image analysis: In these algorithms it is often necessary to set certain thresholds for whether e.g. a cell should count as diseased or not, and the chosen threshold will partly depend on the software designer’s preference between avoiding false positives and (...)
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  20. Mark A. Bedau, Optimal Formulation of Complex Chemical Systems with a Genetic Algorithm.score: 12.0
    We demonstrate a method for optimizing desired functionality in real complex chemical systems, using a genetic algorithm. The chemical systems studied here are mixtures of amphiphiles, which spontaneously exhibit a complex variety of self-assembled molecular aggregations, and the property optimized is turbidity. We also experimentally resolve the fitness landscape in some hyper-planes through the space of possible amphiphile formulations, in order to assess the practicality of our optimization method. Our method shows clear and significant progress after testing only 1 (...)
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  21. C. Gaucherel & S. Bérard (2011). Equation or Algorithm: Differences and Choosing Between Them. Acta Biotheoretica 59 (1):67-79.score: 12.0
    The issue of whether formal reasoning or a computing-intensive approach is the most efficient manner to address scientific questions is the subject of some considerable debate and pertains not only to the nature of the phenomena and processes investigated by scientists, but also the nature of the equation and algorithm objects they use. Although algorithms and equations both rely on a common background of mathematical language and logic, they nevertheless possess some critical differences. They do not refer to the (...)
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  22. Patrick Grim, Evolution of Communication with a Spatialized Genetic Algorithm.score: 12.0
    We extend previous work by modeling evolution of communication using a spatialized genetic algorithm which recombines strategies purely locally. Here cellular automata are used as a spatialized environment in which individuals gain points by capturing drifting food items and are 'harmed' if they fail to hide from migrating predators. Our individuals are capable of making one of two arbitrary sounds, heard only locally by their immediate neighbors. They can respond to sounds from their neighbors by opening their mouths or (...)
     
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  23. Peter Spirtes, An Algorithm for Fast Recovery of Sparse Causal Graphs.score: 12.0
    Previous asymptotically correct algorithms for recovering causal structure from sample probabilities have been limited even in sparse graphs to a few variables. We describe an asymptotically correct algorithm whose complexity for fixed graph connectivity increases polynomially in the number of vertices, and may in practice recover sparse graphs with several hundred variables. From..
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  24. Alejandro López-Rousseau & Timothy Ketelaar (2006). Juliet: If They Do See Thee, They Will Murder Thee. A Satisficing Algorithm for Pragmatic Conditionals. Mind and Society 5 (1):71-77.score: 12.0
    In a recent Mind & Society article, Evans (2005) argues for the social and communicative function of conditional statements. In a related article, we argue for satisficing algorithms for mapping conditional statements onto social domains (Eur J Cogn Psychol 16:807–823,2004). The purpose of the present commentary is to integrate these two arguments by proposing a revised pragmatic cues algorithm for pragmatic conditionals.
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  25. Andrew V. Goldberg, Giuseppe F. Italiano, David S. Johnson & Dorothea Wagner, Algorithm Engineering (Dagstuhl Seminar 13391).score: 12.0
    This report documents the program and the outcomes of Dagstuhl Seminar 13391 "Algorithm Engineering". The algorithm engineering approach consists of a cycle of algorithm design, analysis, implementation, and experimental evaluation, with the aim of bridging the gap between theory and practice in the area of algorithms. This cycle of phases is driven by falsifiable hypotheses validated by experiments. Moreover, real-world instances often have direct impact on this cycle since they often expose modeling and analysis shortcomings. Algorithm (...)
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  26. Ravi Santosh Arvapally & Xiaoqing (2013). Polarisation Assessment in an Intelligent Argumentation System Using Fuzzy Clustering Algorithm for Collaborative Decision Support. Argument and Computation 4 (3):181-208.score: 12.0
    We developed an on-line intelligent argumentation system which facilitates stakeholders in exchanging dialogues. It provides decision support by capturing stakeholders? rationale through arguments. As part of the argumentation process, stakeholders tend to both polarise their opinions and form polarisation groups. The challenging issue of assessing argumentation polarisation had not been addressed in argumentation systems until recently. Arvapally, Liu, and Jiang [(2012), ?Identification of Faction Groups and Leaders in Web-Based Intelligent Argumentation System for Collaborative Decision Support?, in Proceedings of International Conference (...)
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  27. Shane Harwood & Richard Scheines, Genetic Algorithm Search Over Causal Models.score: 12.0
    Shane Harwood and Richard Scheines. Genetic Algorithm Search Over Causal Models.
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  28. Carsten Lutz, Holger Sturm, Frank Wolter & Michael Zakharyaschev (2002). A Tableau Decision Algorithm for Modalized ALC with Constant Domains. Studia Logica 72 (2):199-232.score: 12.0
    The aim of this paper is to construct a tableau decision algorithm for the modal description logic K ALC with constant domains. More precisely, we present a tableau procedure that is capable of deciding, given an ALC-formula with extra modal operators (which are applied only to concepts and TBox axioms, but not to roles), whether is satisfiable in a model with constant domains and arbitrary accessibility relations. Tableau-based algorithms have been shown to be practical even for logics of rather (...)
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  29. Yunfu Shen (1999). Elimination of Algorithmic Quantifiers for Ordered Differential Algebra. Archive for Mathematical Logic 38 (3):139-144.score: 12.0
    In [2], Singer proved that the theory of ordered differential fields has a model completion, i.e, the theory of closed ordered differential fields, CODF. As a result, CODF admits elimination of quantifiers. In this paper we give an algorithm to eliminate the quantifiers of CODF-formulas.
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  30. Kees van Deemter, Albert Gatt, Ielka van der Sluis & Richard Power (2012). Assessing the Incremental Algorithm: A Response to Krahmer Et Al. Cognitive Science 36 (5):842-845.score: 11.0
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  31. Carl van Walraven & Vivek Goel (2002). The Effect of a Hepatitis Serology Testing Algorithm on Laboratory Utilization. Journal of Evaluation in Clinical Practice 8 (3):327-332.score: 11.0
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  32. Qingfu Zhang (2004). On the Convergence of a Factorized Distribution Algorithm with Truncation Selection. Complexity 9 (4):17-23.score: 11.0
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  33. William E. Seager (2003). Yesterday's Algorithm: Penrose and the Godel Argument. Croatian Journal of Philosophy 3 (9):265-273.score: 10.0
    Roger Penrose is justly famous for his work in physics and mathematics but he is _notorious_ for his endorsement of the Gödel argument (see his 1989, 1994, 1997). This argument, first advanced by J. R. Lucas (in 1961), attempts to show that Gödel’s (first) incompleteness theorem can be seen to reveal that the human mind transcends all algorithmic models of it1. Penrose's version of the argument has been seen to fall victim to the original objections raised against Lucas (see Boolos (...)
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  34. Carol E. Cleland (2001). Recipes, Algorithms, and Programs. Minds and Machines 11 (2):219-237.score: 10.0
    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 ineffective on the (...)
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  35. Murray Clarke (1996). Darwinian Algorithms and Indexical Representation. Philosophy of Science 63 (1):27-48.score: 10.0
    In this paper, I argue that accurate indexical representations have been crucial for the survival and reproduction of homo sapiens sapiens. Specifically, I want to suggest that reliable processes have been selected for because of their indirect, but close, connection to true belief during the Pleistocene hunter-gatherer period of our ancestral history. True beliefs are not heritable, reliable processes are heritable. Those reliable processes connected with reasoning take the form of Darwinian Algorithms: a plethora of specialized, domain-specific inference rules designed (...)
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  36. B. Jack Copeland (1996). What is Computation? Synthese 108 (3):335-59.score: 9.0
    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 (...)
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  37. William J. Rapaport & Michael W. Kibby, Contextual Vocabulary Acquisition: From Algorithm to Curriculum.score: 9.0
    Deliberate contextual vocabulary acquisition (CVA) is a reader’s ability to figure out a (not the) meaning for an unknown word from its “context”, without external sources of help such as dictionaries or people. The appropriate context for such CVA is the “belief-revised integration” of the reader’s prior knowledge with the reader’s “internalization” of the text. We discuss unwarranted assumptions behind some classic objections to CVA, and present and defend a computational theory of CVA that we have adapted to a new (...)
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  38. A. G. Baker, Irina Baetu & Robin A. Murphy (2009). Propositional Learning is a Useful Research Heuristic but It is Not a Theoretical Algorithm. Behavioral and Brain Sciences 32 (2):199-200.score: 9.0
    Mitchell et al.'s claim, that their propositional theory is a single-process theory, is illusory because they relegate some learning to a secondary memory process. This renders the single-process theory untestable. The propositional account is not a process theory of learning, but rather, a heuristic that has led to interesting research.
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  39. Amit Hagar & Alexandre Korolev (2006). Quantum Hypercomputability? Minds and Machines 16 (1):87-93.score: 9.0
    A recent proposal to solve the halting problem with the quantum adiabatic algorithm is criticized and found wanting. Contrary to other physical hypercomputers, where one believes that a physical process “computes” a (recursive-theoretic) non-computable function simply because one believes the physical theory that presumably governs or describes such process, believing the theory (i.e., quantum mechanics) in the case of the quantum adiabatic “hypercomputer” is tantamount to acknowledging that the hypercomputer cannot perform its task.
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  40. Daniel C. Dennett, Evolution as Algorithm.score: 9.0
    John Locke offered what he considered a sound a priori argument that Mind must come first, must be the original Cause, not merely an Effect: If, then, there must be something eternal, let us see what sort of Being it must be. And to that it is very obvious to Reason, that it must necessarily be a cogitative Being. For it is as impossible to conceive that ever bare incogitative Matter should produce a thinking intelligent Being, as that nothing should (...)
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  41. S. Kamal Abdali (1976). An Abstraction Algorithm for Combinatory Logic. Journal of Symbolic Logic 41 (1):222-224.score: 9.0
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  42. Valerie Gray Hardcastle (1993). Evolutionary Epistemology as an Overlapping, Interlevel Theory. Biology and Philosophy 8 (2):173-192.score: 9.0
    I examine the branch of evolutionary epistemology which tries to account for the character of cognitive mechanisms in animals and humans by extending the biological theory of evolution to the neurophysiological substrates of cognition. Like Plotkin, I construe this branch as a struggling science, and attempt to characterize the sort of theory one might expect to find this truly interdisciplinary endeavor, an endeavor which encompasses not only evolutionary biology, cognitive psychology, and developmental neuroscience, but also and especially, the computational modeling (...)
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  43. Per Lindström (2006). Note on Some Fixed Point Constructions in Provability Logic. Journal of Philosophical Logic 35 (3):225 - 230.score: 9.0
    We present a quite simple proof of the fixed point theorem for GL. We also use this proof to show that Sambin's algorithm yields a fixed point.
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  44. Nir Fresco (2011). Concrete Digital Computation: What Does It Take for a Physical System to Compute? [REVIEW] Journal of Logic, Language and Information 20 (4):513-537.score: 9.0
    This paper deals with the question: what are the key requirements for a physical system to perform digital computation? Time and again cognitive scientists are quick to employ the notion of computation simpliciter when asserting basically that cognitive activities are computational. They employ this notion as if there was or is a consensus on just what it takes for a physical system to perform computation, and in particular digital computation. Some cognitive scientists in referring to digital computation simply adhere to (...)
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  45. M. W. Bunder (1990). Some Improvements to Turner's Algorithm for Bracket Abstraction. Journal of Symbolic Logic 55 (2):656-669.score: 9.0
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  46. Thomas Donaldson (forthcoming). An Ethical Algorithm. The Ruffin Series in Business Ethics:101-106.score: 9.0
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  47. Miklós Erdélyi-Szabó, László Kálmán & Agi Kurucz (2008). Towards a Natural Language Semantics Without Functors and Operands. Journal of Logic, Language and Information 17 (1):1-17.score: 9.0
    The paper sets out to offer an alternative to the function/argument approach to the most essential aspects of natural language meanings. That is, we question the assumption that semantic completeness (of, e.g., propositions) or incompleteness (of, e.g., predicates) exactly replicate the corresponding grammatical concepts (of, e.g., sentences and verbs, respectively). We argue that even if one gives up this assumption, it is still possible to keep the compositionality of the semantic interpretation of simple predicate/argument structures. In our opinion, compositionality presupposes (...)
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  48. Susan G. Sterrett (2002). Nested Algorithms and the Original Imitation Game Test: A Reply to James Moor. [REVIEW] Minds and Machines 12 (1):131-136.score: 9.0
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  49. Eric Steinhart (2002). Logically Possible Machines. Minds and Machines 12 (2):259-280.score: 9.0
    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 (...)
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  50. Philip Hanson (2001). Darwin's Algorithm, Natural Selective History, and Intentionality Naturalized. Canadian Journal of Philosophy 31 (Supplement):53-83.score: 9.0
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