Results for '*Algorithms'

973 found
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  1.  33
    Genetic Algorithms による航空スケジュール.Adachi Nobue Sato Makihiko - 2001 - Transactions of the Japanese Society for Artificial Intelligence 16:493-500.
    Schedule planning is one of the most crucial issues for any airline company, because the profit of the company directly depends on the efficiency of the schedule. This paper presents a novel scheduling method which solves problems related to time scheduling, fleet assignment and maintenance routing simultaneously by Genetic Algorithms. Every schedule constraint is embeded in the fitness function, which is described as an object oriented model and works as a simulater developing itself over time, and whose solution is executable (...)
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  2.  22
    Algorithms as fetish: Faith and possibility in algorithmic work.Jamie Sherman, Dawn Nafus & Suzanne L. Thomas - 2018 - Big Data and Society 5 (1).
    Algorithms are powerful because we invest in them the power to do things. With such promise, they can transform the ordinary, say snapshots along a robotic vacuum cleaner’s route, into something much more, such as a clean home. Echoing David Graeber’s revision of fetishism, we argue that this easy slip from technical capabilities to broader claims betrays not the “magic” of algorithms but rather the dynamics of their exchange. Fetishes are not indicators of false thinking, but social contracts in material (...)
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  3.  18
    Algorithms: a top-down approach.Rodney R. Howell - 2023 - New Jersey: World Scientific.
    This comprehensive compendium provides a rigorous framework to tackle the daunting challenges of designing correct and efficient algorithms. It gives a uniform approach to the design, analysis, optimization, and verification of algorithms. The volume also provides essential tools to understand algorithms and their associated data structures. This useful reference text describes a way of thinking that eases the task of proving algorithm correctness. Working through a proof of correctness reveals an algorithm's subtleties in a way that a typical description does (...)
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  4.  3
    Learning algorithms versus automatability of Frege systems.Ján Pich & Rahul Santhanam - forthcoming - Journal of Mathematical Logic.
    We connect learning algorithms and algorithms automating proof search in propositional proof systems: for every sufficiently strong, well-behaved propositional proof system [Formula: see text], we prove that the following statements are equivalent, (1) Provable learning. [Formula: see text] proves efficiently that p-size circuits are learnable by subexponential-size circuits over the uniform distribution with membership queries. (2) Provable automatability. [Formula: see text] proves efficiently that [Formula: see text] is automatable by non-uniform circuits on propositional formulas expressing p-size circuit lower bounds. Here, (...)
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  5.  4
    Learning algorithms versus automatability of Frege systems.Ján Pich & Rahul Santhanam - forthcoming - Journal of Mathematical Logic.
    Journal of Mathematical Logic, Ahead of Print. We connect learning algorithms and algorithms automating proof search in propositional proof systems: for every sufficiently strong, well-behaved propositional proof system [math], we prove that the following statements are equivalent, (1) Provable learning. [math] proves efficiently that p-size circuits are learnable by subexponential-size circuits over the uniform distribution with membership queries. (2) Provable automatability. [math] proves efficiently that [math] is automatable by non-uniform circuits on propositional formulas expressing p-size circuit lower bounds. Here, [math] (...)
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  6. Recipes, algorithms, and programs.Carol E. Cleland - 2001 - Minds and Machines 11 (2):219-237.
    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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  7. Quantum algorithms: Philosophical lessons.Amit Hagar - 2007 - Minds and Machines 17 (2):233-247.
    I discuss the philosophical implications that the rising new science of quantum computing may have on the philosophy of computer science. While quantum algorithms leave the notion of Turing-Computability intact, they may re-describe the abstract space of computational complexity theory hence militate against the autonomous character of some of the concepts and categories of computer science.
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  8.  13
    Search algorithms, hidden labour and information control.Paško Bilić - 2016 - Big Data and Society 3 (1).
    The paper examines some of the processes of the closely knit relationship between Google’s ideologies of neutrality and objectivity and global market dominance. Neutrality construction comprises an important element sustaining the company’s economic position and is reflected in constant updates, estimates and changes to utility and relevance of search results. Providing a purely technical solution to these issues proves to be increasingly difficult without a human hand in steering algorithmic solutions. Search relevance fluctuates and shifts through continuous tinkering and tweaking (...)
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  9.  32
    Algorithms as folding: Reframing the analytical focus.Robin Williams, Claes-Fredrik Helgesson, Lukas Engelmann, Jeffrey Christensen, Jess Bier & Francis Lee - 2019 - Big Data and Society 6 (2).
    This article proposes an analytical approach to algorithms that stresses operations of folding. The aim of this approach is to broaden the common analytical focus on algorithms as biased and opaque black boxes, and to instead highlight the many relations that algorithms are interwoven with. Our proposed approach thus highlights how algorithms fold heterogeneous things: data, methods and objects with multiple ethical and political effects. We exemplify the utility of our approach by proposing three specific operations of folding—proximation, universalisation and (...)
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  10.  75
    Identity, profiling algorithms and a world of ambient intelligence.Katja Vries - 2010 - Ethics and Information Technology 12 (1):71-85.
    The tendency towards an increasing integration of the informational web into our daily physical world (in particular in so-called Ambient Intelligent technologies which combine ideas derived from the field of Ubiquitous Computing, Intelligent User Interfaces and Ubiquitous Communication) is likely to make the development of successful profiling and personalization algorithms, like the ones currently used by internet companies such as Amazon, even more important than it is today. I argue that the way in which we experience ourselves necessarily goes through (...)
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  11.  5
    Will Algorithms Win Medals of Honor? Artificial Intelligence, Human Virtues, and the Future of Warfare.William Hasselberger - 2024 - Journal of Military Ethics 23 (3):289-305.
    Artificial Intelligence (AI) is predicted to play an increasingly central role in warfare, with weaponized robots taking over more battlefield operations, and military algorithms mediating in, or substituting for, human decision-making in areas such as intelligence collection and analysis, targeting, and strategic decision-making. The primary focus of this article is the potential impact of the widespread use of AI systems on soldiers and military leaders themselves – namely, on their moral character, skills, emotions, and agency – and on how they (...)
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  12.  21
    Algorithms and adjudication.William Lucy - 2023 - Jurisprudence 15 (3):251-281.
    This essay addresses a version of Jerome Frank’s question – ‘Are Judges Human?’ – asking instead: are human judges necessary? It begins, in section II, by outlining the technological developments which inform the view that they are not and critically evaluates the juristic position that seemingly endorses it. That position is labelled ‘technological evangelism’ and it consists of three claims about law and adjudication: the certainty, determinacy and partiality claims. Section III shows that these three claims are utterly incompatible with (...)
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  13.  23
    Enculturating Algorithms.Rafael Capurro - 2019 - NanoEthics 13 (2):131-137.
    The paper deals with the difference between who and what we are in order to take an ethical perspective on algorithms and their regulation. The present casting of ourselves as homo digitalis implies the possibility of projecting who we are as social beings sharing a world, into the digital medium, thereby engendering what can be called digital whoness, or a digital reification of ourselves. A main ethical challenge for the evolving digital age consists in unveiling this ethical difference, particularly when (...)
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  14.  32
    Algorithms for computing minimal conflicts.S. Luan, L. Magnani & G. Dai - 2006 - Logic Journal of the IGPL 14 (2):391--406.
    In this paper we present some algorithms for computing minimal conflicts. First of all we discuss the relationship between minimal conflicts and minimally inconsistent subsets. Then we introduce an algorithm for computing all minimally inconsistent subsets, which is applied to generating all minimal conflicts. Furthermore, an algorithm for computing all minimal conflicts using structured description is introduced, and its correctness is proved; its time complexity is also shown. The algorithm using structured description terminates in polynomial time for some special system, (...)
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  15. Algorithms, Abstraction and Implementation.C. Foster - 1990 - Academic Press.
  16.  27
    Algorithms for finding coalitions exploiting a new reciprocity condition.Guido Boella, Luigi Sauro & Leendert van der Torre - 2009 - Logic Journal of the IGPL 17 (3):273-297.
    We introduce a reciprocity criterion for coalition formation among goal-directed agents, which we call the indecomposable do-ut-des property. It refines an older reciprocity property, called the do-ut-des or give-to-get property by considering the fact that agents prefer to form coalitions whose components cannot be formed independently. A formal description of this property is provided as well as an analysis of algorithms and their complexity. We provide an algorithm to decide whether a coalition has the desired property, and we show that (...)
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  17.  20
    Adaptive Algorithms for Meta-Induction.Ronald Ortner - 2023 - Journal for General Philosophy of Science / Zeitschrift für Allgemeine Wissenschaftstheorie 54 (3):433-450.
    Work in online learning traditionally considered induction-friendly (e.g. stochastic with a fixed distribution) and induction-hostile (adversarial) settings separately. While algorithms like Exp3 that have been developed for the adversarial setting are applicable to the stochastic setting as well, the guarantees that can be obtained are usually worse than those that are available for algorithms that are specifically designed for stochastic settings. Only recently, there is an increasing interest in algorithms that give (near-)optimal guarantees with respect to the underlying setting, even (...)
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  18.  29
    Quantum algorithms for simulation of quantum chemistry problems by quantum computers: an appraisal.Smriti Sharma - 2022 - Foundations of Chemistry 24 (2):263-276.
    The ideas of quantum simulation and advances in quantum algorithms to solve quantum chemistry problems have been discussed. Theoretical proposals and experimental investigations both have been studied to gauge the extent to which quantum computation has been applied to solve quantum chemical problems till date. The distinctive features and limitations of the application of quantum simulation on chemical systems and current approaches to define and improve upon standard quantum algorithms have been studied in detail. The possibility and consequences of designing (...)
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  19.  10
    Occluded algorithms.Adam Burke - 2019 - Big Data and Society 6 (2).
    Two definitions of algorithm, their uses, and their implied models of computing in society, are reviewed. The first, termed the structural programming definition, aligns more with usage in computer science, and as the name suggests, the intellectual project of structured programming. The second, termed the systemic definition, is more informal and emerges from ethnographic observations of discussions of software in both professional and everyday settings. Specific examples of locating algorithms within modern codebases are shared, as well as code directly impacting (...)
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  20.  77
    Darwinian algorithms and indexical representation.Murray Clarke - 1996 - Philosophy of Science 63 (1):27-48.
    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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  21.  12
    Algorithms, genetics, and populations: The schemata theorem revisited.Freddy Bugge Christiansen & Marcus W. Feldman - 1998 - Complexity 3 (3):57-64.
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  22.  28
    Memetic algorithms outperform evolutionary algorithms in multimodal optimisation.Phan Trung Hai Nguyen & Dirk Sudholt - 2020 - Artificial Intelligence 287 (C):103345.
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  23. The Reliability of Randomized Algorithms.D. Fallis - 2000 - British Journal for the Philosophy of Science 51 (2):255-271.
    Recently, certain philosophers of mathematics (Fallis [1997]; Womack and Farach [(1997]) have argued that there are no epistemic considerations that should stop mathematicians from using probabilistic methods to establish that mathematical propositions are true. However, mathematicians clearly should not use methods that are unreliable. Unfortunately, due to the fact that randomized algorithms are not really random in practice, there is reason to doubt their reliability. In this paper, I analyze the prospects for establishing that randomized algorithms are reliable. I end (...)
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  24.  29
    How should we theorize algorithms? Five ideal types in analyzing algorithmic normativities.Lotta Björklund Larsen & Francis Lee - 2019 - Big Data and Society 6 (2).
    The power of algorithms has become a familiar topic in society, media, and the social sciences. It is increasingly common to argue that, for instance, algorithms automate inequality, that they are biased black boxes that reproduce racism, or that they control our money and information. Implicit in many of these discussions is that algorithms are permeated with normativities, and that these normativities shape society. The aim of this editorial is double: First, it contributes to a more nuanced discussion about algorithms (...)
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  25.  20
    The Epistemological Dimension of Algorithms.Wiebke Loosen & Armin Scholl - 2021 - Constructivist Foundations 16 (3):369-371.
    Algorithms play an increasing role within the process of digitalization of society. They are used for descriptions and predictions of consumers’ or citizens’ behavior. Sociologist Elena ….
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  26. An overview of tableau algorithms for description logics.Franz Baader & Ulrike Sattler - 2001 - Studia Logica 69 (1):5-40.
    Description logics are a family of knowledge representation formalisms that are descended from semantic networks and frames via the system Kl-one. During the last decade, it has been shown that the important reasoning problems (like subsumption and satisfiability) in a great variety of description logics can be decided using tableau-like algorithms. This is not very surprising since description logics have turned out to be closely related to propositional modal logics and logics of programs (such as propositional dynamic logic), for which (...)
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  27.  58
    Deterministic Algorithms, Simple Languages And One‐to‐One Gentzen Type Formalizations.Anita Wasilewska - 1986 - Mathematical Logic Quarterly 32 (10-12):181-188.
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  28.  12
    Pruning algorithms for multi-model adversary search.David Carmel & Shaul Markovitch - 1998 - Artificial Intelligence 99 (2):325-355.
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  29.  22
    Implementations, algorithms, and more.John R. Anderson - 1987 - Behavioral and Brain Sciences 10 (3):498-505.
  30.  16
    Styles of Valuation: Algorithms and Agency in High-throughput Bioscience.Claes-Fredrik Helgesson & Francis Lee - 2020 - Science, Technology, and Human Values 45 (4):659-685.
    In science and technology studies today, there is a troubling tendency to portray actors in the biosciences as “cultural dopes” and technology as having monolithic qualities with predetermined outcomes. To remedy this analytical impasse, this article introduces the concept styles of valuation to analyze how actors struggle with valuing technology in practice. Empirically, this article examines how actors in a bioscientific laboratory struggle with valuing the properties and qualities of algorithms in a high-throughput setting and identifies the copresence of several (...)
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  31. Algorithms, affect, and aesthetic listening.David Cecchetto - 2017 - In Marcel Cobussen, Vincent Meelberg & Barry Truax, The Routledge companion to sounding art. Abingdon, Oxon: Routledge.
     
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  32.  38
    Algorithms for sentences over integral domains.Shih Ping Tung - 1990 - Annals of Pure and Applied Logic 47 (2):189-197.
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  33. Evolutionary Algorithms.Jennifer S. Hallinan & Janet Wiles - 2003 - In L. Nadel, Encyclopedia of Cognitive Science. Nature Publishing Group.
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  34.  2
    Negative performance feedback from algorithms or humans? effect of medical researchers’ algorithm aversion on scientific misconduct.Ganli Liao, Feiwen Wang, Wenhui Zhu & Qichao Zhang - 2024 - BMC Medical Ethics 25 (1):1-20.
    Institutions are increasingly employing algorithms to provide performance feedback to individuals by tracking productivity, conducting performance appraisals, and developing improvement plans, compared to traditional human managers. However, this shift has provoked considerable debate over the effectiveness and fairness of algorithmic feedback. This study investigates the effects of negative performance feedback (NPF) on the attitudes, cognition and behavior of medical researchers, comparing NPF from algorithms versus humans. Two scenario-based experimental studies were conducted with a total sample of 660 medical researchers (algorithm (...)
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  35.  25
    Complex Algorithms for Data-Driven Model Learning in Science and Engineering.Francisco J. Montáns, Francisco Chinesta, Rafael Gómez-Bombarelli & J. Nathan Kutz - 2019 - Complexity 2019:1-3.
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  36. Algorithms for user centred, problem driven automated coaching–.M. Hendriks, M. Spit & M. A. Weffers-Albu - 1991 - In Stephen Everson, Psychology: Companions to Ancient Thought, Vol. 2. New York: Cambridge University Press. pp. 16--287.
     
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  37.  19
    Semantic algorithms of bioethics.N. A. Ageeva, N. L. Wiegel & G. N. Shapoval - 2019 - Theoretical Bioethics 24 (2):6-11.
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  38. Accountable algorithms.J. A. Kroll, J. Huey, S. Barocas, E. Felten, J. Reidenberg, D. Robinson & H. Yu - 2017 - University of Pennyslvania Law Review.
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  39.  9
    Algorithms for estimating the partition function of restricted Boltzmann machines.Oswin Krause, Asja Fischer & Christian Igel - 2020 - Artificial Intelligence 278 (C):103195.
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  40. Algorithms and the chinese room.Mark Sprevak - manuscript
     
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  41.  89
    Nested Algorithms and "The Original Imitation Game Test": A Reply to James Moor.Susan G. Sterrett - 2002 - Minds and Machines 12 (1):131-136.
    In "The Status and Future of the Turing Test" (Moor, 2001), which appeared in an earlier issue of this journal, James Moor remarks on my paper "Turing's Two Tests for Intelligence." In my paper I had claimed that, whatever Turing may or may not have thought, the test described in the opening section of Turing's now legendary 1950 paper "Computing Machinery and Intelligence" is not equivalent to, and in fact is superior to, the test described in a passage that occurs (...)
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  42.  31
    Algorithms and Decision Problems: a Crash Course in Recursion Theory.Dirk van Dalen - 1989 - Journal of Symbolic Logic 54 (3):1094-1095.
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  43.  18
    Design and analysis of algorithms: a contemporary perspective.Sandeep Sen - 2019 - New York, NY: University Printing House. Edited by Amit Kumar.
    Focuses on the interplay between algorithm design and the underlying computational models.
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  44.  22
    Encyclopedia of algorithms.Ming-Yang Kao (ed.) - 2007 - London: Springer.
    The online edition supplements this index with hyperlinks as well as including internal hyperlinks to related entries in the text, CrossRef citations, and links ...
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  45.  45
    DFC-algorithms for Suszko logic and one-to-one Gentzen type formalizations.Anita Wasilewska - 1984 - Studia Logica 43 (4):395 - 404.
    We use here the notions and results from algebraic theory of programs in order to give a new proof of the decidability theorem for Suszko logic SCI (Theorem 3).We generalize the method used in the proof of that theorem in order to prove a more general fact that any prepositional logic which admits a cut-free Gentzen type formalization is decidable (Theorem 6).
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  46.  11
    An elementary approach to design and analysis of algorithms.L. R. Vermani - 2019 - New Jersey: World Scientific. Edited by Shalini Vermani.
    In computer science, an algorithm is an unambiguous specification of how to solve a class of problems. Algorithms can perform calculation, data processing and automated reasoning tasks. As an effective method, an algorithm can be expressed within a finite amount of space and time and in a well-defined formal language for calculating a function. Starting from an initial state and initial input (perhaps empty), the instructions describe a computation that, when executed, proceeds through a finite number of well-defined successive states, (...)
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  47.  11
    Techniques for designing and analyzing algorithms.Douglas R. Stinson - 2021 - Boca Raton: C&H\CRC Press.
    Design and analysis of algorithms can be a difficult subject for students due to its sometimes-abstract nature and its use of a wide variety of mathematical tools. Here the author, an experienced and successful textbook writer, makes the subject as straightforward as possible in an up-to-date textbook incorporating various new developments appropriate for an introductory course. This text presents the main techniques of algorithm design, namely, divide-and-conquer algorithms, greedy algorithms, dynamic programming algorithms, and backtracking. Graph algorithms are studied in detail, (...)
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  48.  50
    On the applicability of diploid genetic algorithms.Harsh Bhasin & Sushant Mehta - 2016 - AI and Society 31 (2):01-10.
    The heuristic search processes like simple genetic algorithms help in achieving optimization but do not guarantee robustness so there is an immediate need of a machine learning technique that also promises robustness. Diploid genetic algorithms ensure consistent results and can therefore replace Simple genetic algorithms in applications such as test data generation and regression testing, where robustness is more important. However, there is a need to review the work that has been done so far in the field. It is also (...)
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  49.  22
    Digital, politics, and algorithms: Governing digital data through the lens of data protection.Rocco Bellanova - 2017 - European Journal of Social Theory 20 (3):329-347.
    Many actors mobilize the cognitive, legal and technical tool-box of data protection when they discuss and address controversial issues such as digital mass surveillance. Yet, critical approaches to the digital only barely explore the politics of data protection in relation to data-driven governance. Building on governmentality studies and Actor-Network-Theory, this article analyses the potential and limits of using data protection to critique the ‘digital age’. Using the conceptual tool of dispositifs, it sketches an analytics of data protection and the emergence (...)
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  50.  13
    Isomorphisms of genetic algorithms.David L. Battle & Michael D. Vose - 1993 - Artificial Intelligence 60 (1):155-165.
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