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  1. Introduction to a Systemic Theory of Meaning (Jan 2010 Update).Christophe Menant - manuscript
    Information and Meaning are present everywhere around us and within ourselves. Specific studies have been implemented in order to link information and meaning: - Semiotics - Phenomenology - Analytic Philosophy - Psychology No general coverage is available for the notion of meaning. We propose to complement this lack by a systemic approach to meaning generation.
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  2. Introduction to a Systemic Theory of Meaning (July 2014 Update).Christophe Menant - manuscript
    Information and Meaning are present everywhere around us and within ourselves. Specific studies have been implemented in order to link information and meaning: - Semiotics - Phenomenology - Analytic Philosophy - Psychology No general coverage is available for the notion of meaning. We propose to complement this lack by a systemic approach to meaning generation.
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  3. Information and Meaning in Life, Humans and Robots (FIS 2005).Christophe Menant - manuscript
    Information and meaning exist around us and within ourselves, and the same information can correspond to different meanings. This is true for humans and animals, and is becoming true for robots. We propose here an overview of this subject by using a systemic tool related to meaning generation that has already been published (C. Menant, Entropy 2003). The Meaning Generator System (MGS) is a system submitted to a constraint that generates a meaningful information when it receives an incident information that (...)
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  4. Superposition.Ilexa Yardley - 2021 - Https://Medium.Com/the-Circular-Theory/.
  5. Heterogeneous Proxytypes Extended: Integrating Theory-Like Representations and Mechanisms with Prototypes and Exemplars.Antonio Lieto - 2018 - In Advances in Intelligent Systems and Computing: Proceedings of BICA. Springer.
    The paper introduces an extension of the proposal according to which conceptual representations in cognitive agents should be intended as heterogeneous proxytypes. The main contribution of this paper is in that it details how to reconcile, under a heterogeneous representational perspective, different theories of typicality about conceptual representation and reasoning. In particular, it provides a novel theoretical hypothesis - as well as a novel categorization algorithm called DELTA - showing how to integrate the representational and reasoning assumptions of the theory-theory (...)
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  6. Symbolic Representations of Reality.Ilexa Yardley - 2018 - Https://Medium.Com/the-Circular-Theory/.
  7. Conceptual Spaces for Cognitive Architectures: A Lingua Franca for Different Levels of Representation.Antonio Lieto, Antonio Chella & Marcello Frixione - 2017 - Biologically Inspired Cognitive Architectures 19:1-9.
    During the last decades, many cognitive architectures (CAs) have been realized adopting different assumptions about the organization and the representation of their knowledge level. Some of them (e.g. SOAR [35]) adopt a classical symbolic approach, some (e.g. LEABRA[ 48]) are based on a purely connectionist model, while others (e.g. CLARION [59]) adopt a hybrid approach combining connectionist and symbolic representational levels. Additionally, some attempts (e.g. biSOAR) trying to extend the representational capacities of CAs by integrating diagrammatical representations and reasoning are (...)
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  8. Dual PECCS: A Cognitive System for Conceptual Representation and Categorization.Antonio Lieto, Daniele Radicioni & Valentina Rho - 2017 - Journal of Experimental and Theoretical Artificial Intelligence 29 (2):433-452.
    In this article we present an advanced version of Dual-PECCS, a cognitively-inspired knowledge representation and reasoning system aimed at extending the capabilities of artificial systems in conceptual categorization tasks. It combines different sorts of common-sense categorization (prototypical and exemplars-based categorization) with standard monotonic categorization procedures. These different types of inferential procedures are reconciled according to the tenets coming from the dual process theory of reasoning. On the other hand, from a representational perspective, the system relies on the hypothesis of conceptual (...)
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  9. A Theory of Practical Meaning.Carlotta Pavese - 2017 - Philosophical Topics 45 (2):65-96.
    This essay is divided into two parts. In the first part (§2), I introduce the idea of practical meaning by looking at a certain kind of procedural systems — the motor system — that play a central role in computational explanations of motor behavior. I argue that in order to give a satisfactory account of the content of the representations computed by motor systems (motor commands), we need to appeal to a distinctively practical kind of meaning. Defending the explanatory relevance (...)
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  10. The Orbital Space Environment and Space Situational Awareness Domain Ontology – Towards an International Information System for Space Data.Robert J. Rovetto - 2016 Sept - In Proceedings of The Advanced Maui Optical and Space Surveillance Technologies (AMOS) Conference.
    The orbital space environment is home to natural and artificial satellites, debris, and space weather phenomena. As the population of orbital objects grows so do the potential hazards to astronauts, space infrastructure and spaceflight capability. Orbital debris, in particular, is a universal concern. This and other hazards can be minimized by improving global space situational awareness (SSA). By sharing more data and increasing observational coverage of the space environment we stand to achieve that goal, thereby making spaceflight safer and expanding (...)
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  11. Preliminaries of a Space Situational Awareness Ontology.Robert J. Rovetto & T. S. Kelso - 2016 Feb - In Renato Zanetti, Ryan P. Russell, Martin T. Oximek & Angela L. Bowes (eds.), Proceedings of AAS/AIAA Spaceflight Mechanics Meeting, in Advances in the Astronautical Sciences. Univelt Inc.. pp. 4177-4192.
    Space situational awareness (SSA) is vital for international safety and security, and for the future of space travel. The sharing of SSA data and information should improve the state of global SSA for planetary defense and spaceflight safety. I take steps toward a Space Situational Awareness (SSA) Ontology, and outline some central objectives, requirements and desiderata in the ontology development process for this domain. The purpose of this ontological system is to explore the potential for the ontology research topic to (...)
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  12. Rappresentare i disordini mentali mediante ontologie.Cristina Amoretti, Marcello Frixione & Antonio Lieto - 2016 - Apprendimento, Cognizione E Tecnologia.
    Come è emerso dall’analisi filosofica e dalla ricerca nelle scienze cogni- tive, la maggior parte dei concetti, tra cui molti concetti medici, esibisce degli “effetti prototipici” e non riesce ad essere definita nei termini di condizioni necessarie e sufficienti. Questo aspetto rappresenta un problema per la pro- gettazione di ontologie in informatica, poiché i formalismi adottati per la rap- presentazione della conoscenza (a partire da OWL – Web Ontology Langua- ge) non sono in grado di rendere conto dei concetti nei (...)
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  13. There’s Plenty of Boole at the Bottom: A Reversible CA Against Information Entropy.Francesco Berto, Jacopo Tagliabue & Gabriele Rossi - 2016 - Minds and Machines 26 (4):341-357.
    “There’s Plenty of Room at the Bottom”, said the title of Richard Feynman’s 1959 seminal conference at the California Institute of Technology. Fifty years on, nanotechnologies have led computer scientists to pay close attention to the links between physical reality and information processing. Not all the physical requirements of optimal computation are captured by traditional models—one still largely missing is reversibility. The dynamic laws of physics are reversible at microphysical level, distinct initial states of a system leading to distinct final (...)
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  14. The Space Object Ontology.Robert J. Rovetto - 2016 - 2016 1.
    This paper develops the ontology of space objects for theoretical and computational ontology applied to the space (astronautical/astronomical) domain. It follows “An ontological architecture for Orbital Debris Data” (Rovetto, 2015) and “Preliminaries of a Space Situational Awareness Ontology” (Rovetto, Kelso, 2016). Important considerations for developing a space object ontology, or more broadly, a space domain ontology are presented. The main category term ‘Space Object’ is analyzed from a philosophical perspective. The ontological commitments of legal definitions for artificial space objects are (...)
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  15. Ontology Archtecures for the Orbital Space Environment and Space Situational Awareness Domain.Robert John Rovetto - 2016 - In Stefano Borgo, Loris Bozzato, Chiara Del Vescovo & Martin Homola (eds.), Proceedings of the Joint Ontology Workshops with the 9th International Conference on Formal Ontology in Information Systems. CEUR.
    This paper applies some ontology architectures to the space domain, specifically the orbital and near-earth space environment and the space situational awareness domain. I briefly summarize local, single and hybrid ontology architectures, and offer potential space ontology architectures for each by showing how actual space data sources and space organizations would be involved.
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  16. Quantifiers and Cognition: Logical and Computational Perspectives.Jakub Szymanik - 2016 - Springer.
    This volume on the semantic complexity of natural language explores the question why some sentences are more difficult than others. While doing so, it lays the groundwork for extending semantic theory with computational and cognitive aspects by combining linguistics and logic with computations and cognition. -/- Quantifier expressions occur whenever we describe the world and communicate about it. Generalized quantifier theory is therefore one of the basic tools of linguistics today, studying the possible meanings and the inferential power of quantifier (...)
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  17. An Ontological Architecture for Orbital Debris Data.Robert J. Rovetto - 2015 - Earth Science Informatics 9 (1):67-82.
    The orbital debris problem presents an opportunity for inter-agency and international cooperation toward the mutually beneficial goals of debris prevention, mitigation, remediation, and improved space situational awareness (SSA). Achieving these goals requires sharing orbital debris and other SSA data. Toward this, I present an ontological architecture for the orbital debris and broader SSA domain, taking steps in the creation of an orbital debris ontology (ODO). The purpose of this ontological system is to (I) represent general orbital debris and SSA domain (...)
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  18. A Computational Framework for Concept Representation in Cognitive Systems and Architectures: Concepts as Heterogeneous Proxytypes.Antonio Lieto - 2014 - Proceedings of 5th International Conference on Biologically Inspired Cognitive Architectures, Boston, MIT, Pocedia Computer Science, Elsevier:1-9.
    In this paper a possible general framework for the representation of concepts in cognitive artificial systems and cognitive architectures is proposed. The framework is inspired by the so called proxytype theory of concepts and combines it with the heterogeneity approach to concept representations, according to which concepts do not constitute a unitary phenomenon. The contribution of the paper is twofold: on one hand, it aims at providing a novel theoretical hypothesis for the debate about concepts in cognitive sciences by providing (...)
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  19. Review of Fenstad's "Grammar, Geometry & Brain". [REVIEW]Erich Rast - 2014 - Studia Logica 102 (1):219-223.
    In this small book logician and mathematician Jens Erik Fenstad addresses some of the most important foundational questions of linguistics: What should a theory of meaning look like and how might we provide the missing link between meaning theory and our knowledge of how the brain works? The author’s answer is twofold. On the one hand, he suggests that logical semantics in the Montague tradition and other broadly conceived symbolic approaches do not suffice. On the other hand, he does not (...)
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  20. Representation, Analytic Pragmatism and AI.Raffaela Giovagnoli - 2013 - In Gordana Dodig-Crnkovic Raffaela Giovagnoli (ed.), Computing Nature. pp. 161--169.
    Our contribution aims at individuating a valid philosophical strategy for a fruitful confrontation between human and artificial representation. The ground for this theoretical option resides in the necessity to find a solution that overcomes, on the one side, strong AI (i.e. Haugeland) and, on the other side, the view that rules out AI as explanation of human capacities (i.e. Dreyfus). We try to argue for Analytic Pragmatism (AP) as a valid strategy to present arguments for a form of weak AI (...)
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  21. Eric Winsberg: Science in the Age of Computer Simulation: The University of Chicago Press, Chicago, IL, 2010, 168 Pp., $ 24.00 , ISBN: 978-0-226-90204-3. [REVIEW]Stefan Gruner - 2013 - Minds and Machines 23 (2):251-254.
  22. Shaping Up: The Phenotypic Quality Ontology and Cross Sections.Robert J. Rovetto - 2013 - In Oliver Kutz, Mehul Bhatt, Stefano Borgo & Paulo Santos (eds.), CEUR Workshop Procecedings Vol-1007.
    pplied ontology, philosophical ontology, biomedical ontology, artifacts, cross section, philosophy of mathematics, Phenotypic Quality Ontology, PATO , ontology of shape.
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  23. The Semantic Representation of Natural Language.Michael Levison - 2012 - Bloomsbury Academic.
    Introduction -- Basic concepts -- Previous approaches -- Semantic expressions: introduction -- Formal issues -- Semantic expressions: basic features -- Advanced features -- Applications: capture -- Three little pigs -- Applications: creation.
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  24. Proceedings of the International Conference on Computational Semantics 9.J. Bos & S. Pulman (eds.) - 2011
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  25. Computation on Information, Meaning and Representations. An Evolutionary Approach (World Scientific 2011).Christophe Menant - 2011 - In Dodig-Crnkovic, Gordana & Mark Burgin (eds.), Information and Computation. World Scientific. pp. 255-286.
    Understanding computation as “a process of the dynamic change of information” brings to look at the different types of computation and information. Computation of information does not exist alone by itself but is to be considered as part of a system that uses it for some given purpose. Information can be meaningless like a thunderstorm noise, it can be meaningful like an alert signal, or like the representation of a desired food. A thunderstorm noise participates to the generation of meaningful (...)
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  26. The Shape of Shapes: An Ontological Exploration.Robert Rovetto - 2011 - In Janna Hastings, Oliver Kutz, Mehul Bhatt & Stefano Borgo (eds.), CEUR Workshop Proceedings Vol-812. Editors.
    What are shapes? The ancient Greek geometer is perhaps the most familiar with these entities, but shape is general enough to transcend domains of inquiry. In this communication I present a broad examination of shape and related notions, such as form, boundary and surface. I aim to explore this question and describe the category of shape by identifying some of its most general features. I contrast geometric (or perfect) shapes with physical or organic shapes (the shape of objects), discuss granularity, (...)
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  27. Two Kinds of Procedural Semantics for Privative Modification.Giuseppe Primiero & Bjorn Jespersen - 2010 - Lecture Notes in Artificial Intelligence 6284:251--271.
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  28. Comprehension of Simple Quantifiers: Empirical Evaluation of a Computational Model.Jakub Szymanik & Marcin Zajenkowski - 2010 - Cognitive Science 34 (3):521-532.
    We examine the verification of simple quantifiers in natural language from a computational model perspective. We refer to previous neuropsychological investigations of the same problem and suggest extending their experimental setting. Moreover, we give some direct empirical evidence linking computational complexity predictions with cognitive reality.<br>In the empirical study we compare time needed for understanding different types of quantifiers. We show that the computational distinction between quantifiers recognized by finite-automata and push-down automata is psychologically relevant. Our research improves upon hypothesis and (...)
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  29. Understanding Quantifiers in Language.Jakub Szymanik & Marcin Zajenkowski - 2009 - In N. A. Taatgen & H. van Rijn (eds.), Proceedings of the 31st Annual Conference of the Cognitive Science Society.
    We compare time needed for understanding different types of quantifiers. We show that the computational distinction between quantifiers recognized by finite-automata and pushdown automata is psychologically relevant. Our research improves upon hypothesis and explanatory power of recent neuroimaging studies as well as provides evidence for the claim that human linguistic abilities are constrained by computational complexity.
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  30. What the <0.70, 1.17, 0.99, 1.07> is a Symbol?Istvan S. N. Berkeley - 2008 - Minds and Machines 18 (1):93-105.
    The notion of a ‘symbol’ plays an important role in the disciplines of Philosophy, Psychology, Computer Science, and Cognitive Science. However, there is comparatively little agreement on how this notion is to be understood, either between disciplines, or even within particular disciplines. This paper does not attempt to defend some putatively ‘correct’ version of the concept of a ‘symbol.’ Rather, some terminological conventions are suggested, some constraints are proposed and a taxonomy of the kinds of issue that give rise to (...)
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  31. Robosemantics: How Stanley the Volkswagen Represents the World. [REVIEW]Christopher Parisien & Paul Thagard - 2008 - Minds and Machines 18 (2):169-178.
    One of the most impressive feats in robotics was the 2005 victory by a driverless Volkswagen Touareg in the DARPA Grand Challenge. This paper discusses what can be learned about the nature of representation from the car’s successful attempt to navigate the world. We review the hardware and software that it uses to interact with its environment, and describe how these techniques enable it to represent the world. We discuss robosemantics, the meaning of computational structures in robots. We argue that (...)
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  32. Toward a Pragmatic Understanding of the Cognitive Underpinnings of Symbol Grounding.Ben Goertzel, Moshe Looks, Ari Heljakka & Cassio Pennachin - 2007 - In R. Gudwin & J. Queiroz (eds.), Semiotics and Intelligent Systems Development. Idea Group.
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  33. Towards a Reference Terminology for Ontology Research and Development in the Biomedical Domain.Barry Smith, Waclaw Kusnierczyk, Daniel Schober, & Werner Ceusters - 2006 - In Proceedings of KR-MED, CEUR, vol. 222. pp. 57-65.
    Ontology is a burgeoning field, involving researchers from the computer science, philosophy, data and software engineering, logic, linguistics, and terminology domains. Many ontology-related terms with precise meanings in one of these domains have different meanings in others. Our purpose here is to initiate a path towards disambiguation of such terms. We draw primarily on the literature of biomedical informatics, not least because the problems caused by unclear or ambiguous use of terms have been there most thoroughly addressed. We advance a (...)
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  34. The Gamut of Dynamic Logics.Martin Stokhof & Jan van Eijck - 2006 - In Dov Gabbay & John Woods (eds.), The Handbook of the History of Logic. Volume 6: Logic and Modalities in the Twentieth Century. Elsevier. pp. 499-600.
    Dynamic logic, broadly conceived, is the logic that analyses change by decomposing actions into their basic building blocks and by describing the results of performing actions in given states of the world. The actions studied by dynamic logic can be of various kinds: actions on the memory state of a computer, actions of a moving robot in a closed world, interactions between cognitive agents performing given communication protocols, actions that change the common ground between speaker and hearer in a conversation, (...)
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  35. Representation and Inference for Natural Language: A First Course in Computational Semantics.Patrick Blackburn - 2005 - Center for the Study of Language and Information.
    How can computers distinguish the coherent from the unintelligible, recognize new information in a sentence, or draw inferences from a natural language passage? Computational semantics is an exciting new field that seeks answers to these questions, and this volume is the first textbook wholly devoted to this growing subdiscipline. The book explains the underlying theoretical issues and fundamental techniques for computing semantic representations for fragments of natural language. This volume will be an essential text for computer scientists, linguists, and anyone (...)
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  36. Inference and Computational Semantics.Patrick Blackburn & Michael Kohlhase - 2004 - Journal of Logic, Language and Information 13 (2):117-120.
  37. Computational Semantics in Discourse: Underspecification, Resolution, and Inference.Johan Bos - 2004 - Journal of Logic, Language and Information 13 (2):139-157.
    In this paper I introduce a formalism for natural language understandingbased on a computational implementation of Discourse RepresentationTheory. The formalism covers a wide variety of semantic phenomena(including scope and lexical ambiguities, anaphora and presupposition),is computationally attractive, and has a genuine inference component. Itcombines a well-established linguistic formalism (DRT) with advancedtechniques to deal with ambiguity (underspecification), and isinnovative in the use of first-order theorem proving techniques.The architecture of the formalism for natural language understandingthat I advocate consists of three levels of processing:underspecification, (...)
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  38. Computational Semantics.Patrick Blackburn & Johan Bos - 2003 - Theoria 18 (1):27-45.
    In this article we discuss what constitutes a good choice of semantic representation, compare different approaches of constructing semantic representations for fragments of natural language, and give an overview of recent methods for employing inference engines for natural language understanding tasks.
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  39. Computational Semantics.Patrick Blackburn & Johan Bos - 2003 - Theoria: Revista de Teoría, Historia y Fundamentos de la Ciencia 18 (1):27-45.
    In this article we discuss what constitutes a good choice of semantic representation, compare different approaches of constructing semantic representations for fragments of natural language, and give an overview of recent methods for employing inference engines for natural language understanding tasks.
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  40. Selective Representing and World-Making.Pete Mandik & Andy Clark - 2002 - Minds and Machines 12 (3):383-395.
    In this paper, we discuss the thesis of selective representing — the idea that the contents of the mental representations had by organisms are highly constrained by the biological niches within which the organisms evolved. While such a thesis has been defended by several authors elsewhere, our primary concern here is to take up the issue of the compatibility of selective representing and realism. In this paper we hope to show three things. First, that the notion of selective representing is (...)
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  41. Perceptual Symbols and Taxonomy Comparison.Xiang Chen - 2001 - Philosophy of Science 68 (3):S200-S212.
    Many recent cognitive studies reveal that human cognition is inherently perceptual, sharing systems with perception at both the conceptual and the neural levels. This paper introduces Barsalou's theory of perceptual symbols and explores its implications for philosophy of science. If perceptual symbols lie in the heart of conceptual processing, the process of attribute selection during concept representation, which is critical for defining similarity and thus for comparing taxonomies, can no longer be determined solely by background beliefs. The analogous nature of (...)
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  42. Ambiguous Discourse in a Compositional Context. An Operational Perspective.Tim Fernando - 2001 - Journal of Logic, Language and Information 10 (1):63-86.
    The processing of sequences of (English) sentences is analyzedcompositionally through transitions that merge sentences, rather thandecomposing them. Transitions that are in a precise senseinertial are related to disjunctive and non-deterministic approaches toambiguity. Modal interpretations are investigated, inducing variousequivalences on sequences.
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  43. Representationalism and the Metonymic Fallacy.L. Böök - 1999 - Synthese 118 (1):13-30.
    Representationalism in cognitive science holds that semantic meaning should be explained by representations in the mind or brain. In this paper it is argued that semantic meaning should instead be explained by an abstract theory of semantic machines -- machines with predicative capability. The concept of a semantic machine (like that of a Turing machine or of Dennett's intentional systems ) is not a physical concept -- although it has physical implementations. The predicative competence of semantic machines is defined in (...)
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  44. Representation Operators and Computation.Brendan Kitts - 1999 - Minds and Machines 9 (2):223-240.
    This paper analyses the impact of representation and search operators on Computational Complexity. A model of computation is introduced based on a directed graph, and representation and search are defined to be the vertices and edges of this graph respectively. Changing either the representation or the search algorithm leads to different possible complexity classes. The final section explores the role of representation in reducing time complexity in Artificial Intelligence.
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  45. Foundations of Statistical Natural Language Processing.Christopher Manning & Hinrich Schütze - 1999 - MIT Press.
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  46. Situations and Artificial Intelligence.Varol Akman - 1998 - Minds and Machines 8 (4):475-477.
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  47. Computational Semantics for Monadic Quantifiers.Marcin Mostowski - 1998 - Journal of Applied Non--Classical Logics 8 (1-2):107--121.
    The paper gives a survey of known results related to computational devices (finite and push–down automata) recognizing monadic generalized quantifiers in finite models. Some of these results are simple reinterpretations of descriptive—feasible correspondence theorems from finite–model theory. Additionally a new result characterizing monadic quantifiers recognized by push down automata is proven.
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  48. Action Patterns, Conceptualization, and Artificial Intelligence.Stan Franklin - 1997 - Behavioral and Brain Sciences 20 (1):23-24.
    This commentary connects some of Glenberg's ideas to similar ideas from artificial intelligence. Second, it briefly discusses hidden assumptions relating to meaning, representations, and projectable properties. Finally, questions about mechanisms, mental imagery, and conceptualization in animals are posed.
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  49. How Do Users Know What to Say?Nicole Yankelovich - 1996 - Interactions 3 (6):32-43.
    As speech recognition technology improves, many software designers are being challenged to design speech user inter faces. For me, one of the most challenging issues is “How do users know what they can say?” If you think about it, a speech-only interface poses the same problems as a command-line interface. The functionality of applications is hidden, and the boundaries of what can and cannot be done are invisible. Graphical interfaces were invented largely to make hidden functionality visible to the user. (...)
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  50. Representation, Similarity, and the Chorus of Prototypes.Shimon Edelman - 1995 - Minds and Machines 5 (1):45-68.
    It is proposed to conceive of representation as an emergent phenomenon that is supervenient on patterns of activity of coarsely tuned and highly redundant feature detectors. The computational underpinnings of the outlined concept of representation are (1) the properties of collections of overlapping graded receptive fields, as in the biological perceptual systems that exhibit hyperacuity-level performance, and (2) the sufficiency of a set of proximal distances between stimulus representations for the recovery of the corresponding distal contrasts between stimuli, as in (...)
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