Results for 'Symbolic modeling'

974 found
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  1.  90
    Computational Modelling of Culture and Affect.Ruth Aylett & Ana Paiva - 2012 - Emotion Review 4 (3):253-263.
    This article discusses work on implementing emotional and cultural models into synthetic graphical characters. An architecture, FAtiMA, implemented first in the antibullying application FearNot! and then extended as FAtiMA-PSI in the cultural-sensitivity application ORIENT, is discussed. We discuss the modelling relationships between culture, social interaction, and cognitive appraisal. Integrating a lower level homeostatically based model is also considered as a means of handling some of the limitations of a purely symbolic approach. Evaluation to date is summarised and future directions (...)
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  2.  26
    Cognitive Modeling of Anticipation: Unsupervised Learning and Symbolic Modeling of Pilots' Mental Representations.Sebastian Blum, Oliver Klaproth & Nele Russwinkel - 2022 - Topics in Cognitive Science 14 (4):718-738.
    The ability to anticipate team members' actions enables joint action towards a common goal. Task knowledge and mental simulation allow for anticipating other agents' actions and for making inferences about their underlying mental representations. In human–AI teams, providing AI agents with anticipatory mechanisms can facilitate collaboration and successful execution of joint action. This paper presents a computational cognitive model demonstrating mental simulation of operators' mental models of a situation and anticipation of their behavior. The work proposes two successive steps: (1) (...)
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  3.  39
    Computational modelling of spoken-word recognition processes: design choices and evaluation.Odette Scharenborg & Lou Boves - 2010 - Pragmatics and Cognition 18 (1):136-164.
    Computational modelling has proven to be a valuable approach in developing theories of spoken-word processing. In this paper, we focus on a particular class of theories in which it is assumed that the spoken-word recognition process consists of two consecutive stages, with an `abstract' discrete symbolic representation at the interface between the stages. In evaluating computational models, it is important to bring in independent arguments for the cognitive plausibility of the algorithms that are selected to compute the processes in (...)
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  4. (1 other version)Symbolic construction of reality: Scenography of the world.V. V. Ilin - 2013 - Liberal Arts in Russia 2 (6):531--551.
    The author draws attention to gnoseologically not enough investigated phenomenon of automorphism thinking. In the widely known works of F. Varela, and U. Maturana automorphism is associated mainly with the study of the adaptation of biological organisms. It appears, however, that the possibilities of this approach are more significant. The author believes that the driving force of thinking activity is the constructive combination. Cognitive morphogenesis processes as a free combination of symbolic forms, managed by rules of mental experimentation over (...)
     
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  5.  37
    Rosen's modelling relations via categorical adjunctions.Elias Zafiris - 2012 - International Journal of General Systems 41 (5):439-474.
    Rosen's modelling relations constitute a conceptual schema for the understanding of the bidirectional process of correspondence between natural systems and formal symbolic systems. The notion of formal systems used in this study refers to information structures constructed as algebraic rings of observable attributes of natural systems, in which the notion of observable signifies a physical attribute that, in principle, can be measured. Due to the fact that modelling relations are bidirectional by construction, they admit a precise categorical formulation in (...)
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  6.  16
    Modelling with Words: Learning, Fusion, and Reasoning Within a Formal Linguistic Representation Framework.Jonathan Lawry - 2003 - Springer Verlag.
    Modelling with Words is an emerging modelling methodology closely related to the paradigm of Computing with Words introduced by Lotfi Zadeh. This book is an authoritative collection of key contributions to the new concept of Modelling with Words. A wide range of issues in systems modelling and analysis is presented, extending from conceptual graphs and fuzzy quantifiers to humanist computing and self-organizing maps. Among the core issues investigated are - balancing predictive accuracy and high level transparency in learning - scaling (...)
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  7. Symbolic versus Modelistic Elements in Scientific Modeling.Chuang Liu - 2015 - Theoria: Revista de Teoría, Historia y Fundamentos de la Ciencia 30 (2):287.
    In this paper, we argue that symbols are conventional vehicles whose chief function is denotation, while models are epistemic vehicles, and their chief function is to show what their targets are like in the relevant aspects. And we explain why this is incompatible with the deflationary view on scientific modeling. Although the same object may serve both functions, the two vehicles are conceptually distinct and most models employ both elements. With the clarification of this point we offer an alternative (...)
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  8. Cognitive Modelling and Conceptual Spaces.Antonio Lieto - 2021 - Airbus Invited Talks on Cognitive Modelling.
    I will present the rationale followed for the conceptualization and the following development the Dual PECCS system that relies on the cognitively grounded heterogeneous proxytypes representational hypothesis. Such hypothesis allows integrating exemplars and prototype theories of categorization and has provided useful insights in the context of cognitive modelling for what concerns the typicality effects in categorization. As argued in [Chella et al., 2017] [Lieto et al., 2018b] [Lieto et al., 2018a] a pivotal role in this respect is played by the (...)
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  9.  20
    A. H. Kruse. A method of modelling the formalism of set theory in axiomatic set theory. The journal of symbolic logic, vol. 28 no. 1 , pp. 20–34. [REVIEW]Azriel Lévy - 1966 - Journal of Symbolic Logic 31 (1):132-133.
  10.  37
    (1 other version)A method of modelling the formalism of set theory in axiomatic set theory.A. H. Kruse - 1963 - Journal of Symbolic Logic 28 (1):20-34.
    As is well known, some paradoxes arise through inadequate analysis of the meanings of terms in a language, an adequate analysis showing that the paradoxes arise through a lack of separation of an object theory and a metatheory. Under such an adequate analysis in which parts of the metatheory are modelled in the object theory, the paradoxes give way to remarkable theorems establishing limitations of the object theory.Such a modelling is often accomplished by a Gödel numbering. Here we shall use (...)
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  11. Connecting object to symbol in modeling cognition.Stevan Harnad - 1992 - In A. Clark & Ronald Lutz (eds.), Connectionism in Context. Springer Verlag. pp. 75--90.
    Connectionism and computationalism are currently vying for hegemony in cognitive modeling. At first glance the opposition seems incoherent, because connectionism is itself computational, but the form of computationalism that has been the prime candidate for encoding the "language of thought" has been symbolic computationalism (Dietrich 1990, Fodor 1975, Harnad 1990c; Newell 1980; Pylyshyn 1984), whereas connectionism is nonsymbolic (Fodor & Pylyshyn 1988, or, as some have hopefully dubbed it, "subsymbolic" Smolensky 1988). This paper will examine what is and (...)
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  12.  23
    Instrumental, conceptual and symbolic effects of data use: the impact of collaboration and expectations.Roos Van Gasse, Jan Vanhoof & Peter Van Petegem - 2017 - Educational Studies 44 (5):521-534.
    The contribution of data use in schools has been proven via visible changes in policy and practice in schools, changes in practitioners learning or cognition and changes in opinions or attitudes regarding teaching or policy-making. Nevertheless, limited research is available on the extent to which data use in schools results in the aforementioned effects and how they can be explained by data use expectations and collaboration. This paper addresses both issues by describing and explaining data use effects via a large-scale (...)
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  13.  27
    Introduction to logic for systems modelling.Václav Pinkava - 1988 - Cambridge: Abacus Press.
  14.  26
    In Silico Medicine: Social, Technological and Symbolic Mediation.Annamaria Carusi - 2016 - Humana Mente 9 (30).
    In silico medicine is still forging a road for itself in the current biomedical landscape. Discursively and rhetorically, it is using a three-way positioning, first, deploying discourses of personalised medicine, second, extending the 3Rs from animal to clinical research, and third, aligning its methods with experimental methods. The discursive and rhetorical positioning in promotions and statements of the programme gives us insight into the sociability of the scientific labour of advancing the programme. Its progress depends on complex social, institutional and (...)
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  15. Nominal Conceptualism and Logical Modelling of Agents’ Conceptions.Farshad Badie - 2021 - Логико-Философские Штудии 1 (19):95-100.
    In the view of my philosophical position “nominal conceptualism”, cognitive/knowledge agents, who are in some way aware of expressing the world based on their mental concepts, deal with their linguistic and/or symbolic expressions. In this paper I rely on nominal conceptualism to logically characterise agents’ concept-based descriptions of the world and analyse a fundamental logical system for conception representation.
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  16.  37
    Organizing centres and symbolic dynamic in the study of mixed-mode oscillations generated by models of biological autocatalytic processes.P. Tracqui - 1994 - Acta Biotheoretica 42 (2-3):147-166.
    The organization of the complex mixed-mode oscillations generated, in a three-dimensional variable space, by an autocatalytic process formalized as a cubic monomial is analyzed. The generation of the temporal patterns is elucidated by complementary approaches dealing with the three-variable differential continuous system itself and with successive discrete applications modelling its first return map. The extent to which the underlying bifurcation structures could constitute a fingerprint of autocatalytic processes is discussed in connection with the modelling of biological systems.
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  17.  51
    Reasoning in Non-probabilistic Uncertainty: Logic Programming and Neural-Symbolic Computing as Examples.Tarek R. Besold, Artur D’Avila Garcez, Keith Stenning, Leendert van der Torre & Michiel van Lambalgen - 2017 - Minds and Machines 27 (1):37-77.
    This article aims to achieve two goals: to show that probability is not the only way of dealing with uncertainty ; and to provide evidence that logic-based methods can well support reasoning with uncertainty. For the latter claim, two paradigmatic examples are presented: logic programming with Kleene semantics for modelling reasoning from information in a discourse, to an interpretation of the state of affairs of the intended model, and a neural-symbolic implementation of input/output logic for dealing with uncertainty in (...)
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  18.  25
    Reasoning in Non-probabilistic Uncertainty: Logic Programming and Neural-Symbolic Computing as Examples.Henri Prade, Markus Knauff, Igor Douven & Gabriele Kern-Isberner - 2017 - Minds and Machines 27 (1):37-77.
    This article aims to achieve two goals: to show that probability is not the only way of dealing with uncertainty ; and to provide evidence that logic-based methods can well support reasoning with uncertainty. For the latter claim, two paradigmatic examples are presented: logic programming with Kleene semantics for modelling reasoning from information in a discourse, to an interpretation of the state of affairs of the intended model, and a neural-symbolic implementation of input/output logic for dealing with uncertainty in (...)
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  19. (1 other version)The symbol grounding problem.Stevan Harnad - 1990 - Physica D 42:335-346.
    There has been much discussion recently about the scope and limits of purely symbolic models of the mind and about the proper role of connectionism in cognitive modeling. This paper describes the symbol grounding problem : How can the semantic interpretation of a formal symbol system be made intrinsic to the system, rather than just parasitic on the meanings in our heads? How can the meanings of the meaningless symbol tokens, manipulated solely on the basis of their shapes, (...)
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  20.  62
    Modeling and experimenting: The combinatorial strategy in synthetic biology.Tarja Knuuttila & Andrea Loettgers - unknown
    In which respects do modeling and experimenting resemble or differ from each other? We explore this question through studying in detail the combinatorial strategy in synthetic biology whereby scientists triangulate experimentation on model organisms, mathematical modeling, and synthetic modeling. We argue that this combinatorial strategy is due to the characteristic constraints of the three epistemic activities. Moreover, our case study shows that in some cases materiality clearly matters, in fact it provides the very rationale of synthetic (...). We will show how the materialities of the different kinds of models – biological components versus mathematical symbols – in combination with their different structures – the complexity of biological organisms versus the isolated network structure and its mathematical dynamics - define the spectrum of epistemic possibilities in synthetic biology. Furthermore, our case shows that from the perspective of scientific practice the question of whether or not simulations are like or unlike experiments is often beside the point, since they are used to accomplish different kinds of things. (shrink)
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  21.  18
    The Challenge of Modeling the Acquisition of Mathematical Concepts.Alberto Testolin - 2020 - Frontiers in Human Neuroscience 14:511878.
    As a full-blown research topic, numerical cognition is investigated by a variety of disciplines including cognitive science, developmental and educational psychology, linguistics, anthropology and, more recently, biology and neuroscience. However, despite the great progress achieved by such a broad and diversified scientific inquiry, we are still lacking a comprehensive theory that could explain how numerical concepts are learned by the human brain. In this perspective, I argue that computer simulation should have a primary role in filling this gap because it (...)
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  22. Dynamic mechanistic explanation: computational modeling of circadian rhythms as an exemplar for cognitive science.William Bechtel & Adele Abrahamsen - 2010 - Studies in History and Philosophy of Science Part A 41 (3):321-333.
    Two widely accepted assumptions within cognitive science are that (1) the goal is to understand the mechanisms responsible for cognitive performances and (2) computational modeling is a major tool for understanding these mechanisms. The particular approaches to computational modeling adopted in cognitive science, moreover, have significantly affected the way in which cognitive mechanisms are understood. Unable to employ some of the more common methods for conducting research on mechanisms, cognitive scientists’ guiding ideas about mechanism have developed in conjunction (...)
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  23.  60
    Extending SME to Handle Large‐Scale Cognitive Modeling.Kenneth D. Forbus, Ronald W. Ferguson, Andrew Lovett & Dedre Gentner - 2017 - Cognitive Science 41 (5):1152-1201.
    Analogy and similarity are central phenomena in human cognition, involved in processes ranging from visual perception to conceptual change. To capture this centrality requires that a model of comparison must be able to integrate with other processes and handle the size and complexity of the representations required by the tasks being modeled. This paper describes extensions to Structure-Mapping Engine since its inception in 1986 that have increased its scope of operation. We first review the basic SME algorithm, describe psychological evidence (...)
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  24.  38
    Alan Bundy. The computer modelling of mathematical reasoning. Academic Press, London etc. 1983, xiv + 322 pp. [REVIEW]Vladimir Lifschitz - 1987 - Journal of Symbolic Logic 52 (2):555-557.
  25. The Place of Modeling in Cognitive Science.James L. McClelland - 2009 - Topics in Cognitive Science 1 (1):11-38.
    I consider the role of cognitive modeling in cognitive science. Modeling, and the computers that enable it, are central to the field, but the role of modeling is often misunderstood. Models are not intended to capture fully the processes they attempt to elucidate. Rather, they are explorations of ideas about the nature of cognitive processes. In these explorations, simplification is essential—through simplification, the implications of the central ideas become more transparent. This is not to say that simplification (...)
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  26.  47
    Modeling Parallelization and Flexibility Improvements in Skill Acquisition: From Dual Tasks to Complex Dynamic Skills.Niels Taatgen - 2005 - Cognitive Science 29 (3):421-455.
    Emerging parallel processing and increased flexibility during the acquisition of cognitive skills form a combination that is hard to reconcile with rule‐based models that often produce brittle behavior. Rule‐based models can exhibit these properties by adhering to 2 principles: that the model gradually learns task‐specific rules from instructions and experience, and that bottom‐up processing is used whenever possible. In a model of learning perfect time‐sharing in dual tasks (Schumacher et al., 2001), speedup learning and bottom‐up activation of instructions can explain (...)
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  27.  71
    An active symbols theory of chess intuition.Alexandre Linhares - 2005 - Minds and Machines 15 (2):131-181.
    The well-known game of chess has traditionally been modeled in artificial intelligence studies by search engines with advanced pruning techniques. The models were thus centered on an inference engine manipulating passive symbols in the form of tokens. It is beyond doubt, however, that human players do not carry out such processes. Instead, chess masters instead carry out perceptual processes, carefully categorizing the chunks perceived in a position and gradually building complex dynamic structures to represent the subtle pressures embedded in the (...)
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  28.  40
    Kabakov F. A.. O modélirovanii po réalizuémosti psévdobulévyh algébr. Doklady Akadémii Nauk SSSR, vol. 192 , pp. 16–18.Kabakov F. A.. On modelling of pseudo-Boolean algebras by realizability. English translation of the preceding by M. Machover. Soviet mathematics, vol. 11 no. 3 , pp. 562–564. [REVIEW]Gene F. Rose - 1972 - Journal of Symbolic Logic 37 (3):627-628.
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  29.  55
    Human Symbol Manipulation Within an Integrated Cognitive Architecture.John R. Anderson - 2005 - Cognitive Science 29 (3):313-341.
    This article describes the Adaptive Control of Thought–Rational (ACT–R) cognitive architecture (Anderson et al., 2004; Anderson & Lebiere, 1998) and its detailed application to the learning of algebraic symbol manipulation. The theory is applied to modeling the data from a study by Qin, Anderson, Silk, Stenger, & Carter (2004) in which children learn to solve linear equations and perfect their skills over a 6‐day period. Functional MRI data show that: (a) a motor region tracks the output of equation solutions, (...)
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  30.  29
    Modeling How, When, and What Is Learned in a Simple Fault‐Finding Task.Frank E. Ritter & Peter A. Bibby - 2008 - Cognitive Science 32 (5):862-892.
    We have developed a process model that learns in multiple ways while finding faults in a simple control panel device. The model predicts human participants' learning through its own learning. The model's performance was systematically compared to human learning data, including the time course and specific sequence of learned behaviors. These comparisons show that the model accounts very well for measures such as problem‐solving strategy, the relative difficulty of faults, and average fault‐finding time. More important, because the model learns and (...)
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  31.  12
    Symbolic and Cognitive Theory in Biology.Sean O. Nuallain - 2014 - Cosmos and History 10 (1):183-210.
    In previous work, I have looked in detail at the capacity and the limits of the linguistics model as applied to gene expression. The recent use of a primitive applied linguistic model in Apple's SIRI system allows further analysis. In particular, the failings of this system resemble those of the HGP; the model used also helps point out the shortcomings of the concept of the "gene". This is particularly urgent as we are entering an era of applied biology in the (...)
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  32. Modeling the Emergence of Language as an Embodied Collective Cognitive Activity.Edwin Hutchins & Christine M. Johnson - 2009 - Topics in Cognitive Science 1 (3):523-546.
    Two decades of attempts to model the emergence of language as a collective cognitive activity have demonstrated a number of principles that might have been part of the historical process that led to language. Several models have demonstrated the emergence of structure in a symbolic medium, but none has demonstrated the emergence of the capacity for symbolic representation. The current shift in cognitive science toward theoretical frameworks based on embodiment is already furnishing computational models with additional mechanisms relevant (...)
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  33. Modeling ancient and modern arithmetic practices: Addition and multiplication with Arabic and Roman numerals.Dirk Schlimm & Hansjörg Neth - 2008 - In B. C. Love, K. McRae & V. M. Sloutsky (eds.), Proceedings of the 30th Annual Conference of the Cognitive Science Society. Cognitive Science Society. pp. 2097--2102.
    To analyze the task of mental arithmetic with external representations in different number systems we model algorithms for addition and multiplication with Arabic and Roman numerals. This demonstrates that Roman numerals are not only informationally equivalent to Arabic ones but also computationally similar—a claim that is widely disputed. An analysis of our models' elementary processing steps reveals intricate tradeoffs between problem representation, algorithm, and interactive resources. Our simulations allow for a more nuanced view of the received wisdom on Roman numerals. (...)
     
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  34.  26
    Modeling community garden participation: how locations and frames shape participant demographics.Katie L. Butterfield - 2023 - Agriculture and Human Values 40 (3):1067-1085.
    Ample research documents the health benefits of community gardens, but our understanding of the factors shaping gardener participation is limited. Neighborhood demographics and garden frames have each been theorized to play a role in shaping who participates in community gardens. Yet, our understanding of the interplay between these factors is underdeveloped and this body of work lacks consideration of the racial and class makeup of gardeners on a large scale. With a nation-wide survey that includes measures of gardener demographics (N (...)
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  35.  92
    Modeling occurrences of objects in relations.L. E. O. Joop - 2010 - Review of Symbolic Logic 3 (1):145-174.
    We study the logical structure of relations, and in particular the notion of occurrences of objects in a state. We start with formulating a number of principles for occurrences and defining corresponding mathematical models. These models are analyzed to get more insight in the formal properties of occurrences. In particular, we prove uniqueness results that tell us more about the possible logical structures relations might have.
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  36.  15
    A Symbolic Framing of Exploitative Firms: Evidence from Japan.Jungwon Min - 2023 - Journal of Business Ethics 190 (3):589-605.
    Symbols can be used to mask or embellish firms’ exploitative labor practices. The present study defines exploitative firms’ abuse of symbolic management using legitimate symbolic terminologies to embellish their demanding working conditions as symbolic framing and examines it in the Japanese context. Because of strong social criticism for exploitative practices, firms are under pressure to avoid giving an exploitative impression to stakeholders, particularly job seekers in recruitment. This study argues that exploitative firms respond to these pressures by (...)
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  37. Categorical Modeling of Natural Complex Systems. Part I: Functorial Process of Representation.Elias Zafiris - 2008 - Advances in Systems Science and Applications 8 (2):187-200.
    We develop a general covariant categorical modeling theory of natural systems’ behavior based on the fundamental functorial processes of representation and localization-globalization. In the first part of this study we analyze the process of representation. Representation constitutes a categorical modeling relation that signifies the semantic bidirectional process of correspondence between natural systems and formal symbolic systems. The notion of formal systems is substantiated by algebraic rings of observable attributes of natural systems. In this perspective, the distinction between (...)
     
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  38. Exploring Minds: Modes of Modeling and Simulation in Artificial Intelligence.Hajo Greif - 2021 - Perspectives on Science 29 (4):409-435.
    The aim of this paper is to grasp the relevant distinctions between various ways in which models and simulations in Artificial Intelligence (AI) relate to cognitive phenomena. In order to get a systematic picture, a taxonomy is developed that is based on the coordinates of formal versus material analogies and theory-guided versus pre-theoretic models in science. These distinctions have parallels in the computational versus mimetic aspects and in analytic versus exploratory types of computer simulation. The proposed taxonomy cuts across the (...)
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  39. Prakken Henry. Logical tools for modelling legal argument. A study of defeasible reasoning in law. Law and philosophy library, vol. 32. Kluwer Academic Publishers, Dordrecht, Boston, and London, 1997, xiii + 314 pp. [REVIEW]R. P. Loui - 1999 - Journal of Symbolic Logic 64 (4):1840-1841.
  40.  24
    Modeling Abstractness and Metaphoricity.Jonathan Dunn - 2015 - Metaphor and Symbol 30 (4):259-289.
    This paper presents and evaluates a model of how the abstractness of source and target concepts influences metaphoricity, the property of how metaphoric a linguistic metaphoric expression is. The purpose of this is to investigate the long-standing claim that metaphoric mappings are from less abstract concepts to more abstract concepts. First, abstractness is modeled using Searle’s social ontology and this model of abstractness evaluated using a participant-based measure of abstractness. Second, this model of abstractness is used to determine the direction (...)
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  41.  68
    Peter Gärdenfors. Knowledge in flux. Modeling the dynamics of epistemic states. Bradford books. The MIT Press, Cambridge, Mass., and London, 1988, xi + 262 pp. - Carlos E. Alchourrón, Peter Gärdenfors, and David Makinson. On the logic of theory change: partial meet contraction and revision functions. The journal of symbolic logic, vol. 50 , pp. 510–530. [REVIEW]Andre Fuhrmann - 1992 - Journal of Symbolic Logic 57 (4):1479-1481.
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  42.  49
    Grounding symbols in the analog world with neural nets a hybrid model.Stevan Harnad - unknown
    1.1 The predominant approach to cognitive modeling is still what has come to be called "computationalism" (Dietrich 1990, Harnad 1990b), the hypothesis that cognition is computation. The more recent rival approach is "connectionism" (Hanson & Burr 1990, McClelland & Rumelhart 1986), the hypothesis that cognition is a dynamic pattern of connections and activations in a "neural net." Are computationalism and connectionism really deeply different from one another, and if so, should they compete for cognitive hegemony, or should they collaborate? (...)
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  43. Modeling the concept of truth using the largest intrinsic fixed point of the strong Kleene three valued semantics (in Croatian language).Boris Culina - 2004 - Dissertation, University of Zagreb
    The thesis deals with the concept of truth and the paradoxes of truth. Philosophical theories usually consider the concept of truth from a wider perspective. They are concerned with questions such as - Is there any connection between the truth and the world? And, if there is - What is the nature of the connection? Contrary to these theories, this analysis is of a logical nature. It deals with the internal semantic structure of language, the mutual semantic connection of sentences, (...)
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  44.  16
    Revealing Complex Ecological Dynamics via Symbolic Regression.Yize Chen, Marco Tulio Angulo & Yang-Yu Liu - 2019 - Bioessays 41 (12):1900069.
    Understanding the dynamics of complex ecosystems is a necessary step to maintain and control them. Yet, reverse-engineering ecological dynamics remains challenging largely due to the very broad class of dynamics that ecosystems may take. Here, this challenge is tackled through symbolic regression, a machine learning method that automatically reverse-engineers both the model structure and parameters from temporal data. How combining symbolic regression with a “dictionary” of possible ecological functional responses opens the door to correctly reverse-engineering ecosystem dynamics, even (...)
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  45.  23
    What is modeling for?Terry Regier - 1997 - Behavioral and Brain Sciences 20 (1):34-34.
    What would Glenberg 's attractive ideas look like when computationally fleshed out? I suggest that the most helpful next step in formalizing them is neither a connectionist nor a symbolic implementation, but rather an implementation- general analysis of the task in terms of the informational content required.
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  46.  70
    Models as icons: modeling models in the semiotic framework of Peirce’s theory of signs.Björn Kralemann & Claas Lattmann - 2013 - Synthese 190 (16):3397-3420.
    In this paper, we try to shed light on the ontological puzzle pertaining to models and to contribute to a better understanding of what models are. Our suggestion is that models should be regarded as a specific kind of signs according to the sign theory put forward by Charles S. Peirce, and, more precisely, as icons, i.e. as signs which are characterized by a similarity relation between sign (model) and object (original). We argue for this (1) by analyzing from a (...)
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  47.  19
    Existence of modeling limits for sequences of sparse structures.Jaroslav Nešetřil & Patrice Ossona de Mendez - 2019 - Journal of Symbolic Logic 84 (2):452-472.
  48.  76
    Qualitative probabilities for default reasoning, belief revision, and causal modeling.Moisés Goldszmidt & Judea Pearl - 1996 - Artificial Intelligence 84 (1-2):57-112.
    This paper presents a formalism that combines useful properties of both logic and probabilities. Like logic, the formalism admits qualitative sentences and provides symbolic machinery for deriving deductively closed beliefs and, like probability, it permits us to express if-then rules with different levels of firmness and to retract beliefs in response to changing observations. Rules are interpreted as order-of-magnitude approximations of conditional probabilities which impose constraints over the rankings of worlds. Inferences are supported by a unique priority ordering on (...)
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  49.  12
    Artificial Intelligence and Symbolic Computation: International Conference AISC 2000 Madrid, Spain, July 17-19, 2000. Revised Papers.International Conference Aisc & John A. Campbell - 2001 - Springer Verlag.
    This book constitutes the thoroughly refereed post-proceedings of the International Conference on Artificial Intelligence and Symbolic Computation, AISC 2000, held in Madrid, Spain in July 2000. The 17 revised full papers presented together with three invited papers were carefully reviewed and revised for inclusion in the book. Among the topics addressed are automated theorem proving, logical reasoning, mathematical modeling of multi-agent systems, expert systems and machine learning, computational mathematics, engineering, and industrial applications.
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  50.  17
    Recurrent Fuzzy-Neural MIMO Channel Modeling.Abhijit Mitra & Kandarpa Kumar Sarma - 2012 - Journal of Intelligent Systems 21 (2):121-142.
    . Fuzzy systems and artificial neural networks, as important components of soft-computation, can be applied together to model uncertainty. A composite block of the fuzzy system and the ANN shares a mutually beneficial association resulting in enhanced performance with smaller networks. It makes them suitable for application with time-varying multi-input multi-output channel modeling enabling such a system to track minute variations in propagation conditions. Here we propose a fuzzy neural system using a fuzzy time delay fully recurrent neural network (...)
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