Results for 'artificial semiotic systems'

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  1. Semiotic Brains and Artificial Minds. How Brains Make Up Material Cognitive Systems.L. Magnani - 2007 - In R. Gudwin & J. Queiroz (eds.), Semiotics and Intelligent Systems Development. Idea Group. pp. 1--41.
  2.  11
    Semiotics of the Artificial: The ‘Self’ of Self-Reproducing Systems in Cellular Automata.Arantza Etxeberria & Jesús Ibáñez - 1999 - Semiotica 127 (1-4):295-320.
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    Beyond Mind: How Brains Make Up Artificial Cognitive Systems[REVIEW]Lorenzo Magnani - 2009 - Minds and Machines 19 (4):477-493.
    What I call semiotic brains are brains that make up a series of signs and that are engaged in making or manifesting or reacting to a series of signs: through this semiotic activity they are at the same time engaged in “being minds” and so in thinking intelligently. An important effect of this semiotic activity of brains is a continuous process of disembodiment of mind that exhibits a new cognitive perspective on the mechanisms underling the semiotic (...)
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    Modeling Life: A Note on the Semiotics of Emergence and Computation in Artificial and Natural Living Systems.Claus Emmeche - forthcoming - Biosemiotics: The Semiotic Web 1991.
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  5. Semiotic. Brains. And. Artificial Minds: How Brains Make Up Material.Lorenzo Magnan - 2007 - In R. Gudwin & J. Queiroz (eds.), Semiotics and Intelligent Systems Development. Idea Group.
  6. Artificial Intelligence: Its Scope and Limits.James H. Fetzer - 1990 - Kluwer Academic Publishers.
    1. WHAT IS ARTIFICIAL INTELLIGENCE? One of the fascinating aspects of the field of artificial intelligence (AI) is that the precise nature of its subject ..
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  7.  91
    The Emergence of Symbol-Based Communication in a Complex System of Artificial Creatures.Angelo Loula, Ricardo Gudwin, Charbel El-Hani & João Queiroz - unknown
    We present here a digital scenario to simulate the emergence of self-organized symbol-based communication among artificial creatures inhabiting a virtual world of predatory events. In order to design the environment and creatures, we seek theoretical and empirical constraints from C.S.Peirce Semiotics and an ethological case study of communication among animals. Our results show that the creatures, assuming the role of sign users and learners, behave collectively as a complex system, where self-organization of communicative interactions plays a major role in (...)
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    On a Cognitive Model of Semiosis.Piotr Konderak - 2015 - Studies in Logic, Grammar and Rhetoric 40 (1):129-144.
    What is the class of possible semiotic systems? What kinds of systems could count as such systems? The human mind is naturally considered the prototypical semiotic system. During years of research in semiotics the class has been broadened to include i.e. living systems like animals, or even plants. It is suggested in the literature on artificial intelligence that artificial agents are typical examples of symbol-processing entities. It also seems that semiotic processes (...)
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  9.  46
    The Semiotic Dynamics of Colour.Luc Steels & Tony Belpaeme - 2005 - Behavioral and Brain Sciences 28 (4):515-524.
    The interesting and deep commentaries on our target article reflect the continued high interest in the problem of colour categorisation and naming. Clearly, colour remains for many cognitive science related disciplines a fascinating microworld in which some of the most fundamental issues for cognition and culture can be studied. Although our target article took the stance of practically oriented engineers who are trying to find the best solution for orchestrating the self-organisation of communication systems in artificial agents, most (...)
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  10.  25
    Experimental Semiotics: A New Approach for Studying the Emergence and the Evolution of Human Communication.Bruno Galantucci & Simon Garrod - 2010 - Interaction Studies: Social Behaviour and Communication in Biological and Artificial Systems 11 (1):1-13.
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  11.  35
    Peirce and the Philosophy of Artificial Intelligence.James Fetzer - 2004 - The Commens Encyclopedia: The Digital Encyclopedia of Peirce Studies.
    A philosophical appraisal of historical positions on the nature of thought, mentality, and intelligence, this survey begins with the views of Descartes, Turing, and Newell and Simon, but includes the work of Haugeland, Fodor, Searle, and other major scholars. The underlying issues concern distinctions between syntax, semantics, and pragmatics, where physical computers seem to be best viewed as mark-manipulating or syntax-processing mechanisms. Alternative accounts have been advanced of what it takes to be a thinking thing, including being Turing machines, symbol (...)
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  12.  12
    Artificial intelligence, culture and education.Sergey B. Kulikov & Anastasiya V. Shirokova - 2021 - AI and Society 36 (1):305-318.
    Sequential transformative design of research :224–235, 2015; Groleau et al. in J Mental Health 16:731–741, 2007; Robson and McCartan in Real world research: a resource for users of social research methods in applied settings, Wiley, Chichester, 2016) allows testing a group of theoretical assumptions about the connections of artificial intelligence with culture and education. In the course of research, semiotics ensures the description of self-organizing systems of cultural signs and symbols in terms of artificial intelligence as a (...)
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  13. Semiotic Systems, Computers, and the Mind: How Cognition Could Be Computing.William J. Rapaport - 2012 - International Journal of Signs and Semiotic Systems 2 (1):32-71.
    In this reply to James H. Fetzer’s “Minds and Machines: Limits to Simulations of Thought and Action”, I argue that computationalism should not be the view that (human) cognition is computation, but that it should be the view that cognition (simpliciter) is computable. It follows that computationalism can be true even if (human) cognition is not the result of computations in the brain. I also argue that, if semiotic systems are systems that interpret signs, then both humans (...)
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  14.  93
    How Minds Can Be Computational Systems.William J. Rapaport - 1998 - Journal of Experimental and Theoretical Artificial Intelligence 10 (4):403-419.
    The proper treatment of computationalism, as the thesis that cognition is computable, is presented and defended. Some arguments of James H. Fetzer against computationalism are examined and found wanting, and his positive theory of minds as semiotic systems is shown to be consistent with computationalism. An objection is raised to an argument of Selmer Bringsjord against one strand of computationalism, namely, that Turing-Test± passing artifacts are persons, it is argued that, whether or not this objection holds, such artifacts (...)
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  15.  46
    Interactive Fiction: Artificial Intelligence as a Mode of Sign Production.Peter Bøgh Andersen & Berit Holmqvist - 1989 - AI and Society 4 (4):291-313.
    Interactive media need their own idioms that exploit the characteristics of the computer based sign. The fact that the reader can physically influence the course of events in the system changes the author's role, since he no longer creates a linear text but anarrative space that the reader can use to generate stories. Although stories are not simulations of the real world, they must still contain recognizable parts where everyday constraints of time and space hold. AI-techniques can be used to (...)
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  16.  10
    Semiotic systems with duality of patterning and the issue of cultural replicators.Gerhard Schaden & Cédric Patin - 2017 - History and Philosophy of the Life Sciences 40 (1):4.
    Two major works in recent evolutionary biology have in different ways touched upon the issue of cultural replicators in language, namely Dawkins’ Selfish Gene and Maynard Smith and Szathmáry’s Major Transitions in Evolution. In the latter, the emergence of language is referred to as the last major transition in evolution, a claim we argue to be derived from a crucial property of language, called Duality of Patterning. Prima facie, this property makes natural language look like a structural equivalent to DNA, (...)
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  17.  19
    Semiotic Systems of Works of Visual Art: Signs, Connotations, Signals.Georgij Yu Somov - 2005 - Semiotica 2005 (157):1-34.
    The analysis of works of visual art illustrates typical groups of elements and interrelations, which form semiotic systems of these works. Specific systems of connotations and their relations with semantic structures, paradigmatics, and typical signal structures are described. Like in linguistic texts, different levels are formed in complex images. The following basic level types are distinguished: sems and other units of semantic level; signs subdivided into: icons of represented objects and connotative sign formations; representamens of basic signs (...)
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  18. On Peirce’s Pragmatic Notion of Semiosis—A Contribution for the Design of Meaning Machines.João Queiroz & Floyd Merrell - 2009 - Minds and Machines 19 (1):129-143.
    How to model meaning processes (semiosis) in artificial semiotic systems? Once all computer simulation becomes tantamount to theoretical simulation, involving epistemological metaphors of world versions, the selection and choice of models will dramatically compromise the nature of all work involving simulation. According to the pragmatic Peircean based approach, semiosis is an interpreter-dependent process that cannot be dissociated from the notion of a situated (and actively distributed) communicational agent. Our approach centers on the consideration of relevant properties and (...)
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  19. Expert Systems and the Abductive Circle.G. Luger & C. Stern - 1993 - In René J. Jorna, Barend van Heusden & Roland Posner (eds.), Signs, Search and Communication: Semiotic Aspects of Artificial Intelligence. De Gruyter. pp. 151-171.
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  20. Challenges for Artificial Cognitive Systems.Antoni Gomila & Vincent C. Müller - 2012 - Journal of Cognitive Science 13 (4):452-469.
    The declared goal of this paper is to fill this gap: “... cognitive systems research needs questions or challenges that define progress. The challenges are not (yet more) predictions of the future, but a guideline to what are the aims and what would constitute progress.” – the quotation being from the project description of EUCogII, the project for the European Network for Cognitive Systems within which this formulation of the ‘challenges’ was originally developed (http://www.eucognition.org). So, we stick out (...)
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  21.  1
    Systems of Logical Representation and Inference: An Empiricist Approach to Cognitive Science.E. M. Barth - 1993 - In René J. Jorna, Barend van Heusden & Roland Posner (eds.), Signs, Search and Communication: Semiotic Aspects of Artificial Intelligence. De Gruyter. pp. 48-65.
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  22.  90
    Human Language and Other Semiotic Systems.Noam Chomsky - 1979 - Semiotica 25 (1-2).
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  23. Artificial Cognition Systems.A. Loula, R. Gudwin & J. Queiroz (eds.) - 2006 - Idea Group Publishers.
  24.  20
    Artificial Cognitive Systems: Where Does Argumentation Fit In?John Fox - 2011 - Behavioral and Brain Sciences 34 (2):78-79.
    Mercier and Sperber (M&S) suggest that human reasoning is reflective and has evolved to support social interaction. Cognitive agents benefit from being able to reflect on their beliefs whether they are acting alone or socially. A formal framework for argumentation that has emerged from research on artificial cognitive systems that parallels M&S's proposals may shed light on mental processes that underpin social interactions.
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  25. Foundations for Active Multimedia Narrative: Semiotic Spaces and Structural Blending.Joseph Goguen & Fox Harrell - forthcoming - Interaction Studies: Social Behaviour and Communication in Biological and Artificial Systems.
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  26.  21
    Artificial Immune System–Negative Selection Classification Algorithm for Four Class Electroencephalogram Signals.Nasir Rashid, Javaid Iqbal, Fahad Mahmood, Anam Abid, Umar S. Khan & Mohsin I. Tiwana - 2018 - Frontiers in Human Neuroscience 12.
  27.  10
    Simplest Semiotic Systems and Plot Typology.B. F. Ègorov - 1974 - Semiotica 10 (2).
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    Russian verse: Its metrics, versification systems, and prosody.Michail Lotman - 2000 - Sign Systems Studies 28:217-240.
    Russian verse: Its metrics, versification systems, and prosody (Generative synopsis). In the article the general verse metre theory and its application to Russian verse is adressed, allowing us, thereby, to observe not the single details, but only the most general characteristics of verse. The treatment can be summarised in the five following points:1) the basis for the phenomenon of verse is its metrical code: the special feature of verse text is the presence of its metre (this feature is common (...)
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  29.  7
    Semiotic Systemity of Visual Artworks: Case Study of The Holy Trinity by Rublev.Georgij Yu Somov - 2007 - Semiotica 2007 (166):105-180.
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  30.  2
    Artificial Immune Systems Can Find Arbitrarily Good Approximations for the NP-Hard Number Partitioning Problem.Dogan Corus, Pietro S. Oliveto & Donya Yazdani - 2019 - Artificial Intelligence 274:180-196.
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  31. On Knowledge Representing Interacting Systems.A. Müller - 1993 - In René J. Jorna, Barend van Heusden & Roland Posner (eds.), Signs, Search and Communication: Semiotic Aspects of Artificial Intelligence. De Gruyter. pp. 271-305.
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  32.  10
    Connotations in Semiotic Systems of Visual Art.Georgij Yu Somov - 2006 - Semiotica 2006 (158):147-212.
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  33.  1
    The Semiotic System of Events, Intrinsic Temporal and Deictic Tense Relations in Natural Language. On the Conceptualization of Temporal Schemata.L. I. Komloszi - 1998 - Poznan Studies in the Philosophy of the Sciences and the Humanities 62:269-286.
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  34.  14
    Verse as a Semiotic System.Mihhail Lotman - 2012 - Sign Systems Studies 40 (1/2):18-51.
    Poetry is an important challenge for semiotics, and a special area of study for the Tartu-Moscow semiotic school, since the first volume of Sign Systems Studies was Juri Lotman’s monograph Lectures on Structural Poetics. From then on the concept of poetry as one of the secondary modelling systems has evolved, since in relation to poetry, the primary modelling system is natural language. In this paper, the concept of semiotic system has been re-examined and the treatment of (...)
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  35.  1
    Phase Transitions in Artificial Intelligence Systems.Bernardo A. Huberman & Tad Hogg - 1987 - Artificial Intelligence 33 (2):155-171.
  36.  11
    Mapping a Network of Semiotic Systems: The Romanian Love Charms Database.Sanda Golopentia - 1997 - Semiotica 114 (1-2):41-66.
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  37.  45
    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.
  38.  7
    Scientific Theories as Meta-Semiotic Systems.Myrna Gopnik - 1977 - Semiotica 21 (3-4).
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  39. Machine Mentality and the Nature of the Ground Relation.Darren Whobrey - 2001 - Minds and Machines 11 (3):307-346.
    John Searle distinguished between weak and strong artificial intelligence (AI). This essay discusses a third alternative, mild AI, according to which a machine may be capable of possessing a species of mentality. Using James Fetzer's conception of minds as semiotic systems, the possibility of what might be called ``mild AI'' receives consideration. Fetzer argues against strong AI by contending that digital machines lack the ground relationship required of semiotic systems. In this essay, the implementational nature (...)
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  40.  7
    Structures and Semiotic Systems.Georgij Yu Somov - 2007 - Semiotica 2007 (167):387-421.
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  41.  5
    Autocommunication in Semiotic Systems: 40 Years After the Theses on the Semiotic Study of Cultures. [REVIEW]Anti Randviir - 2013 - Sign Systems Studies 41 (2/3):378-382.
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  42.  3
    Visual Texture as a Semiotic System.José Luis Caivano - 1990 - Semiotica 80 (3-4):239-252.
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  43.  6
    Hybrid Artificial Intelligence System in Constraint Based Scheduling of Integrated Manufacturing ERP Systems.Izabela Rojek & Mieczysław Jagodziński - 2012 - In Emilio Corchado, Vaclav Snasel, Ajith Abraham, Michał Woźniak, Manuel Grana & Sung-Bae Cho (eds.), Hybrid Artificial Intelligent Systems. Springer. pp. 229--240.
  44. Bounded Rationality and Heuristics in Humans and in Artificial Cognitive Systems.Antonio Lieto - 2019 - Isonomía. Revista de Teoría y Filosofía Del Derecho 1 (4):1-21.
    In this paper I will present an analysis of the impact that the notion of “bounded rationality”, introduced by Herbert Simon in his book “Administrative Behavior”, produced in the field of Artificial Intelligence (AI). In particular, by focusing on the field of Automated Decision Making (ADM), I will show how the introduction of the cognitive dimension into the study of choice of a rational (natural) agent, indirectly determined - in the AI field - the development of a line of (...)
     
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  45.  9
    A New Argument for No-Fault Compensation in Health Care: The Introduction of Artificial Intelligence Systems.Søren Holm, Catherine Stanton & Benjamin Bartlett - 2021 - Health Care Analysis 29 (3):171-188.
    Artificial intelligence systems advising healthcare professionals will be widely introduced into healthcare settings within the next 5–10 years. This paper considers how this will sit with tort/negligence based legal approaches to compensation for medical error. It argues that the introduction of AI systems will provide an additional argument pointing towards no-fault compensation as the better legal solution to compensation for medical error in modern health care systems. The paper falls into four parts. The first part rehearses (...)
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  46.  5
    A Description of a Semiotic System with Simple Syntax.M. I. Lekomceva & B. A. Uspensky - 1976 - Semiotica 18 (2).
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  47.  71
    Why Build a Virtual Brain? Large-Scale Neural Simulations as Test-Bed for Artificial Computing Systems.Matteo Colombo - 2015 - In D. C. Noelle, R. Dale, A. S. Warlaumont, J. Yoshimi, T. Matlock, C. D. Jennings & P. P. Maglio (eds.), Proceedings of the 37th Annual Conference of the Cognitive Science Society. Cognitive Science Society. pp. 429-434.
    Despite the impressive amount of financial resources invested in carrying out large-scale brain simulations, it is controversial what the payoffs are of pursuing this project. The present paper argues that in some cases, from designing, building, and running a large-scale neural simulation, scientists acquire useful knowledge about the computational performance of the simulating system, rather than about the neurobiological system represented in the simulation. What this means, why it is not a trivial lesson, and how it advances the literature on (...)
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  48. What Decision Theory Provides the Best Procedure for Identifying the Best Action Available to a Given Artificially Intelligent System?Samuel A. Barnett - 2018 - Dissertation, University of Oxford
    Decision theory has had a long-standing history in the behavioural and social sciences as a tool for constructing good approximations of human behaviour. Yet as artificially intelligent systems (AIs) grow in intellectual capacity and eventually outpace humans, decision theory becomes evermore important as a model of AI behaviour. What sort of decision procedure might an AI employ? In this work, I propose that policy-based causal decision theory (PCDT), which places a primacy on the decision-relevance of predictors and simulations of (...)
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  49.  73
    A Semiotical Reflection on Biology, Living Signs and Artificial Life.Claus Emmeche - 1991 - Biology and Philosophy 6 (3):325-340.
    It is argued, that theory sf signs, especially in the tradition of the great philosopher Charles Sanders Peirce (1839–1914) can inspire the study of central problems in the philosophy of biology. Three such problems are considered: (1) The nature of biology as a science, where a semiotically informed pluralistic approach to the theory of science is introduced. (2) The peculiarity of the general object of biology, where a realistic interpretation of sign- and information-concepts is required to see sign-processes as immanent (...)
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  50.  14
    The Architecture Challenge: Future Artificial-Intelligence Systems Will Require Sophisticated Architectures, and Knowledge of the Brain Might Guide Their Construction.Gianluca Baldassarre, Vieri Giuliano Santucci, Emilio Cartoni & Daniele Caligiore - 2017 - Behavioral and Brain Sciences 40.
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