Results for 'distributed intelligence'

978 found
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  1. Connectionist representations for natural language: Old and new Noel E. sharkey department of computer science university of exeter.Localist V. Distributed - 1990 - In G. Dorffner (ed.), Konnektionismus in Artificial Intelligence Und Kognitionsforschung. Berlin: Springer-Verlag. pp. 252--1.
  2. Keith S. Decker.Intelligence Testbeds - 1996 - In N. Jennings & G. O'Hare (eds.), Foundations of Distributed Artificial Intelligence. Wiley. pp. 9--119.
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  3. Jacques Ferber.Reactive Distributed Artificial - 1996 - In N. Jennings & G. O'Hare (eds.), Foundations of Distributed Artificial Intelligence. Wiley. pp. 287.
     
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  4.  49
    Distributive justice and cognitive enhancement in lower, normal intelligence.Mikael Dunlop & Julian Savulescu - 2014 - Monash Bioethics Review 32 (3-4):189-204.
    There exists a significant disparity within society between individuals in terms of intelligence. While intelligence varies naturally throughout society, the extent to which this impacts on the life opportunities it affords to each individual is greatly undervalued. Intelligence appears to have a prominent effect over a broad range of social and economic life outcomes. Many key determinants of well-being correlate highly with the results of IQ tests, and other measures of intelligence, and an IQ of 75 (...)
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  5. Michael Wooldridge.Modeling Distributed Artificial - 1996 - In N. Jennings & G. O'Hare (eds.), Foundations of Distributed Artificial Intelligence. Wiley. pp. 269.
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  6.  11
    Intelligent Coordination Distribution of the Whole Supply Chain Based on the Internet of Things.Hongxiu Cui - 2021 - Complexity 2021:1-12.
    In this paper, through the intelligent research of the whole process of logistics and distribution with the Internet of Things supply chain, we study how to improve the development of the cold chain, reduce the loss in circulation, improve the social and economic benefits, and carry out intelligent information collection, monitoring, management, and information tracing of the whole cold chain. This paper analyzes and empirically studies the impact of key technologies of the Internet of Things in cold chain coordination from (...)
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  7.  85
    Distributed artificial intelligence from a socio-cognitive standpoint: Looking at reasons for interaction. [REVIEW]Maria Miceli, Amedo Cesta & Paola Rizzo - 1995 - AI and Society 9 (4):287-320.
    Distributed Artificial Intelligence (DAI) deals with computational systems where several intelligent components interact in a common environment. This paper is aimed at pointing out and fostering the exchange between DAI and cognitive and social science in order to deal with the issues of interaction, and in particular with the reasons and possible strategies for social behaviour in multi-agent interaction is also described which is motivated by requirements of cognitive plausibility and grounded the notions of power, dependence and help. (...)
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  8. A distributed artificial intelligence reading of Todorov's The Conquest of America.J. E. Doran - 1990 - In Tadeusz Buksiński (ed.), Interpretation in the humanities. Poznań: Uniwersytet im. Adama Mickiewicza w Poznaniu.
  9.  53
    Distributed Systems, Parallel Processing, and the Intelligent Computer.D. Frank Hsu - 1986 - Thought: Fordham University Quarterly 61 (4):401-411.
  10.  12
    Reactive distributed artificial intelligence: Principles and applications.Jacques Ferber - 1996 - In N. Jennings & G. O'Hare (eds.), Foundations of Distributed Artificial Intelligence. Wiley. pp. 287--314.
  11.  93
    Ambient Intelligence, Criminal Liability and Democracy.Mireille Hildebrandt - 2008 - Criminal Law and Philosophy 2 (2):163-180.
    In this contribution we will explore some of the implications of the vision of Ambient Intelligence (AmI) for law and legal philosophy. AmI creates an environment that monitors and anticipates human behaviour with the aim of customised adaptation of the environment to a person’s inferred preferences. Such an environment depends on distributed human and non-human intelligence that raises a host of unsettling questions around causality, subjectivity, agency and (criminal) liability. After discussing the vision of AmI we will (...)
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  12.  21
    IDOCS: Intelligent distributed ontology consensus system - The use of machine learning in retinal drusen phenotyping.George Thomas, Michael A. Grassi, John R. Lee, Albert O. Edwards, Michael B. Gorin, Ronald Klein, Thomas L. Casavant, Todd E. Scheetz, Edwin M. Stone & Andrew B. Williams - unknown
    PurposeTo use the power of knowledge acquisition and machine learning in the development of a collaborative computer classification system based on the features of age-related macular degeneration (AMD).MethodsA vocabulary was acquired from four AMD experts who examined 100 ophthalmoscopic images. The vocabulary was analyzed, hierarchically structured, and incorporated into a collaborative computer classification system called IDOCS. Using this system, three of the experts examined images from a second set of digital images compiled from more than 1000 patients with AMD. Images (...)
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  13.  12
    Distributed artificial intelligence.Zhongzhi Shi - 1991 - In P. A. Flach (ed.), Future Directions in Artificial Intelligence. New York: Elsevier Science.
  14.  94
    Philosophy and distributed artificial intelligence: The case of joint intention.Raimo Tuomela - 1996 - In N. Jennings & G. O'Hare (eds.), Foundations of Distributed Artificial Intelligence. Wiley.
    In current philosophical research the term 'philosophy of social action' can be used - and has been used - in a broad sense to encompass the following central research topics: 1) action occurring in a social context; this includes multi-agent action; 2) joint attitudes (or "we-attitudes" such as joint intention, mutual belief) and other social attitudes needed for the explication and explanation of social action; 3) social macro-notions, such as actions performed by social groups and properties of social groups such (...)
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  15.  53
    Distributed artificial intelligence and social science: Critical issues.Cristiano Castelfranchi & Rosaria Conte - 1996 - In N. Jennings & G. O'Hare (eds.), Foundations of Distributed Artificial Intelligence. Wiley.
  16.  7
    Intelligent distributed and networking systems.Jacek Maitan - 1991 - In P. A. Flach (ed.), Future Directions in Artificial Intelligence. New York: Elsevier Science.
  17.  22
    Organizational intelligence and distributed artificial intelligence.Stefan Kirn - 1996 - In N. Jennings & G. O'Hare (eds.), Foundations of Distributed Artificial Intelligence. Wiley.
  18.  18
    Hybrid artificial intelligence approaches on vehicle routing problem in logistics distribution.Dragan Simić & Svetlana Simić - 2012 - In Emilio Corchado, Vaclav Snasel, Ajith Abraham, Michał Woźniak, Manuel Grana & Sung-Bae Cho (eds.), Hybrid Artificial Intelligent Systems. Springer. pp. 208--220.
  19.  8
    Planning in distributed artificial intelligence.Edmund Durfee - 1996 - In N. Jennings & G. O'Hare (eds.), Foundations of Distributed Artificial Intelligence. Wiley. pp. 245.
  20.  17
    Applications of distributed artificial intelligence in industry.H. Van Dyke Parunak - 1996 - In N. Jennings & G. O'Hare (eds.), Foundations of Distributed Artificial Intelligence. Wiley. pp. 139-164.
  21.  66
    Coordination techniques for distributed artificial intelligence.Nick R. Jennings - 1996 - In N. Jennings & G. O'Hare (eds.), Foundations of Distributed Artificial Intelligence. Wiley. pp. 187--210.
  22.  19
    An overview of distributed artificial intelligence.Bernard Moulin & Brahim Chaib-Draa - 1996 - In N. Jennings & G. O'Hare (eds.), Foundations of Distributed Artificial Intelligence. Wiley. pp. 1--3.
  23.  37
    A generic distributed simulation system for intelligent agent design and evaluation.John Anderson - forthcoming - Proceedings of the Tenth Conference on Ai, Simulation and Planning, Ais-2000, Society for Computer Simulation International.
  24.  15
    Image Recognition and Simulation Based on Distributed Artificial Intelligence.Tao Fan - 2021 - Complexity 2021:1-11.
    This paper studies the traditional target classification and recognition algorithm based on Histogram of Oriented Gradients feature extraction and Support Vector Machine classification and applies this algorithm to distributed artificial intelligence image recognition. Due to the huge number of images, the general detection speed cannot meet the requirements. We have improved the HOG feature extraction algorithm. Using principal component analysis to perform dimensionality reduction operations on HOG features and doing distributed artificial intelligence image recognition experiments, the (...)
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  25.  56
    ARCHON: A distributed artificial intelligence system for industrial applications.David Cockburn & Nick R. Jennings - 1996 - In N. Jennings & G. O'Hare (eds.), Foundations of Distributed Artificial Intelligence. Wiley. pp. 319--344.
  26.  13
    Logical foundations of distributed artificial intelligence.Eric Werner - 1996 - In N. Jennings & G. O'Hare (eds.), Foundations of Distributed Artificial Intelligence. Wiley. pp. 57--117.
  27.  17
    User design issues for distributed artificial intelligence.Lynne E. Hall - 1996 - In N. Jennings & G. O'Hare (eds.), Foundations of Distributed Artificial Intelligence. Wiley.
  28.  26
    Emotional Intelligence and Personality Traits Based on Academic Performance.Xin Dong, Olga A. Kalugina, Dinara G. Vasbieva & Arslan Rafi - 2022 - Frontiers in Psychology 13:894570.
    The purpose of this study was to examine the role of personality traits on academic performance. Furthermore, this study also aims at exploring the effects of virtual experience (mediator) and emotional intelligence (moderator) between personality traits and academic performance of the students. The findings imply that personality traits are the strong predictors of better academic performance. However, several personality traits do not have a positive impact on the academic performance. The study further suggests that students who have emotional abilities (...)
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  29.  91
    Generalized distributivity operators.Peter Lasersohn - 1998 - Linguistics and Philosophy 21 (1):83-93.
    Presents a series of generalizations of distributivity operators across a type hierarchy, in order to account for collective-distributive ambiguities for non-subject arguments.
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  30. Artificial Intelligence: A Philosophical Introduction.Jack Copeland - 1993 - Wiley-Blackwell.
    Presupposing no familiarity with the technical concepts of either philosophy or computing, this clear introduction reviews the progress made in AI since the inception of the field in 1956. Copeland goes on to analyze what those working in AI must achieve before they can claim to have built a thinking machine and appraises their prospects of succeeding. There are clear introductions to connectionism and to the language of thought hypothesis which weave together material from philosophy, artificial intelligence and neuroscience. (...)
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  31.  13
    Distribution of responsibility for AI development: expert views.Maria Hedlund & Erik Persson - forthcoming - AI and Society:1-13.
    The purpose of this paper is to increase the understanding of how different types of experts with influence over the development of AI, in this role, reflect upon distribution of forward-looking responsibility for AI development with regard to safety and democracy. Forward-looking responsibility refers to the obligation to see to it that a particular state of affairs materialise. In the context of AI, actors somehow involved in AI development have the potential to guide AI development in a safe and democratic (...)
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  32.  24
    Temporal belief logics for modelling distributed artificial intelligence systems.Michael Wooldridge - 1996 - In N. Jennings & G. O'Hare (eds.), Foundations of Distributed Artificial Intelligence. Wiley. pp. 269--286.
  33.  13
    Artificial Intelligence and the Production of Judicial Truth.Joan Rovira Martorell, Ana Gálvez & Francisco Tirado - forthcoming - Theory, Culture and Society.
    The aim of this paper is to present artificial intelligence (AI) as an organ with a role in the production of judicial truth, expanding its objects, changing its procedures and reshaping the distribution of agencies within the judicial organism. To this end, it builds on Michel Foucault’s work on the procedures of truth production and the three subject forms involved: operator, spectator and object. This is then complemented by the general organological perspective proposed by Bernard Stiegler. On the basis (...)
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  34.  34
    Distributed (design) knowledge exchange.Ann Heylighen, Francis Heylighen, Johan Bollen & Mathias Casaer - 2007 - AI and Society 22 (2):145-154.
    Despite the intrinsic complexity of integrating individual, social and technologically supported intelligence, the paper proposes a relatively simple ‘connectionist’ framework for conceptualizing distributed cognitive systems. Shared information sources (documents) are represented as nodes connected by links of variable strength, which increases as the documents co-occur in the usage patterns. This learning procedure captures and exploits its users’ implicit knowledge to help them find relevant information, thus supporting an unconscious form of exchange. These principles are applied to a concrete (...)
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  35.  5
    (1 other version)Artificial Intelligence (AI) and Global Justice.Siavosh Sahebi & Paul Formosa - 2024 - Minds and Machines 35 (1):1-29.
    This paper provides a philosophically informed and robust account of the global justice implications of Artificial Intelligence (AI). We first discuss some of the key theories of global justice, before justifying our focus on the Capabilities Approach as a useful framework for understanding the context-specific impacts of AI on low- to middle-income countries. We then highlight some of the harms and burdens facing low- to middle-income countries within the context of both AI use and the AI supply chain, by (...)
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  36.  13
    IMAGINE: An integrated environment for constructing distributed artificial intelligence systems.Donald D. Steiner - 1996 - In N. Jennings & G. O'Hare (eds.), Foundations of Distributed Artificial Intelligence. Wiley. pp. 345--364.
  37.  24
    Whaling intelligence: news, facts and US-American exploration in the Pacific.Felix Lüttge - 2019 - British Journal for the History of Science 52 (3):425-445.
    This paper investigates the history of a discursive figure that one could call the intelligent whaler. I argue that this figure's success was made possible by the construal and public distribution of whaling intelligence in an important currency of science – facts – in the preparatory phase for the United States Exploring Expedition (1838–1842). The strongest case for the necessity of the enterprise was New England whalers who were said to cruise uncharted parts of the oceans and whose discoveries (...)
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  38. (1 other version)Future progress in artificial intelligence: A survey of expert opinion.Vincent C. Müller & Nick Bostrom - 2016 - In Vincent C. Müller (ed.), Fundamental Issues of Artificial Intelligence. Cham: Springer. pp. 553-571.
    There is, in some quarters, concern about high–level machine intelligence and superintelligent AI coming up in a few decades, bringing with it significant risks for humanity. In other quarters, these issues are ignored or considered science fiction. We wanted to clarify what the distribution of opinions actually is, what probability the best experts currently assign to high–level machine intelligence coming up within a particular time–frame, which risks they see with that development, and how fast they see these developing. (...)
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  39.  28
    The intelligent technology of smart fishing using a heterogeneous ensemble of unmanned vehicles.Sherstjuk V. G., Zharikova M. V., Sokol I. V., Levkivskyi R. M., Gusev V. N. & Dorovskaja I. O. - 2020 - Artificial Intelligence Scientific Journal 25 (2):71-85.
    The paper addresses the use of heterogeneous ensembles of intelligent unmanned vehicles in such a perspective field of innovations as an unmanned fishery. The issues of joint activity of unmanned vehicles of different types in fishing operations based on intelligent technologies are investigated. The “smart fishing” approach based on the joint fishing operation model is proposed. The operational framework that includes missions, roles, and activity scenarios embedded in the discretized spatial model is presented. The scenario activities are considered as the (...)
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  40.  75
    The ethics of artificial intelligence, UNESCO and the African Ubuntu perspective.Dorine Eva van Norren - 2023 - Journal of Information, Communication and Ethics in Society 21 (1):112-128.
    PurposeThis paper aims to demonstrate the relevance of worldviews of the global south to debates of artificial intelligence, enhancing the human rights debate on artificial intelligence (AI) and critically reviewing the paper of UNESCO Commission on the Ethics of Scientific Knowledge and Technology (COMEST) that preceded the drafting of the UNESCO guidelines on AI. Different value systems may lead to different choices in programming and application of AI. Programming languages may acerbate existing biases as a people’s worldview is (...)
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  41.  7
    Learning from others: Exchange of classification rules in intelligent distributed systems.Dominik Fisch, Martin Jänicke, Edgar Kalkowski & Bernhard Sick - 2012 - Artificial Intelligence 187-188 (C):90-114.
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  42.  28
    The role of biosemiosis and semiotic scaffolding in the processes of developing intelligent behaviour.Anna Sarosiek - 2021 - Philosophical Problems in Science 70:9-44.
    Biosemiotics deals with the processes of signs in all dimensions of nature. Semiosis is the primary form of intelligence. Intelligent behaviour becomes immediately understandable in this approach because semiosis combines causality with the triadic structure of the semiotic sign. Intelligence is a process created in a given context. In the course of evolution organisms have learned to create increasingly sophisticated internal representations of external state. Semiosis is the precursor of the emergence of a feature we consider intelligence. (...)
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  43. Distributed Identity.Phillip Barron - 2022 - Dissertation, University of Connecticut
    This dissertation offers and defends a phenomenological account of personal identity. It does so critically in conversation with Anglo-analytical traditions and varieties of other philosophical traditions from around the world, especially Zen Buddhism. Chapter One brings together three areas of philosophy: the multiple realizability thesis from philosophy of science, the logical pluralist position from philosophical logic, and the various conceptions of personhood from metaphysics. I argue that even though the divide in the literature on the metaphysics of personal identity is (...)
     
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  44.  40
    The Age of Artificial Intelligences: A Personal Reflection.Rafael` Capurro - 2020 - International Review of Information Ethics 28.
    The following paper presents both a historical and personal account of the societal and ethical issues arising in the development of artificial intelligence, tracking, where I was involved, the issues from the nineteen seventies onward. My own involvement in the AI narrative begins with the early discussions around whether machines can think. These first discussions, in time, evolved secondly, with the rise of the internet in the nineties, into perceptions of AI as distributed intelligence, addressing its impact (...)
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  45. Intelligence as Accurate Prediction.Trond A. Tjøstheim & Andreas Stephens - 2022 - Review of Philosophy and Psychology 1 (2):475-499.
    This paper argues that intelligence can be approximated by the ability to produce accurate predictions. It is further argued that general intelligence can be approximated by context dependent predictive abilities combined with the ability to use working memory to abstract away contextual information. The flexibility associated with general intelligence can be understood as the ability to use selective attention to focus on specific aspects of sensory impressions to identify patterns, which can then be used to predict events (...)
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  46.  72
    Application of artificial intelligence: risk perception and trust in the work context with different impact levels and task types.Uwe Klein, Jana Depping, Laura Wohlfahrt & Pantaleon Fassbender - 2024 - AI and Society 39 (5):2445-2456.
    Following the studies of Araujo et al. (AI Soc 35:611–623, 2020) and Lee (Big Data Soc 5:1–16, 2018), this empirical study uses two scenario-based online experiments. The sample consists of 221 subjects from Germany, differing in both age and gender. The original studies are not replicated one-to-one. New scenarios are constructed as realistically as possible and focused on everyday work situations. They are based on the AI acceptance model of Scheuer (Grundlagen intelligenter KI-Assistenten und deren vertrauensvolle Nutzung. Springer, Wiesbaden, 2020) (...)
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  47. Artificial Intelligence: A Philosophical Introduction.B. Jack Copeland - 1993 - Cambridge: Blackwell.
    Presupposing no familiarity with the technical concepts of either philosophy or computing, this clear introduction reviews the progress made in AI since the inception of the field in 1956. Copeland goes on to analyze what those working in AI must achieve before they can claim to have built a thinking machine and appraises their prospects of succeeding.There are clear introductions to connectionism and to the language of thought hypothesis which weave together material from philosophy, artificial intelligence and neuroscience. John (...)
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  48. Rawls, responsibility, and distributive justice.Richard Arneson - manuscript
    The theory of justice pioneered by John Rawls explores a simple idea--that the concern of distributive justice is to compensate individuals for misfortune. Some people are blessed with good luck, some are cursed with bad luck, and it is the responsibility of society--all of us regarded collectively--to alter the distribution of goods and evils that arises from the jumble of lotteries that constitutes human life as we know it. Some are lucky to be born wealthy, or into a favorable socializing (...)
     
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  49.  21
    Intelligence and the developing human brain.Philip Shaw - 2007 - Bioessays 29 (10):962-973.
    Determining the brain properties that make people ‘brainier’ has moved well beyond early demonstrations that increasing intelligence correlates with increasing grey and white matter volumes. Both structural and functional in vivo neuroimaging techniques delineate a distributed network of brain regions, perhaps with a focus in the lateral prefrontal cortex, which varies in extent and connectivity with individual differences in intelligence. Longitudinal studies further show that the neuroanatomic correlates of intelligence are dynamic, changing most rapidly in early (...)
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  50.  9
    Distribution, Recognition, and Just Medical AI.Zachary Daus - 2025 - Philosophy and Technology 38 (1):1-17.
    Medical artificial intelligence (AI) systems are value-laden technologies that can simultaneously encourage and discourage conflicting values that may all be relevant for the pursuit of justice. I argue that the predominant theory of healthcare justice, the Rawls-inspired approach of Norman Daniels, neither adequately acknowledges such conflicts nor explains if and how they can resolved. By juxtaposing Daniels’s theory of healthcare justice with Axel Honneth’s and Nancy Fraser’s respective theories of justice, I draw attention to one such conflict. Medical AI (...)
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