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  1. Intentionality and Artificial Intelligence.Evandro Agazzi - 1981 - Epistemologia 4:195.
  2. Situations and Artificial Intelligence.Varol Akman - 1998 - Minds and Machines 8 (4):475-477.
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  3. The Theory of Parsing, Translation and Compiling.Aho Alfred & Jeffrey Ullman - 1972 - Prentice-Hall.
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  4. 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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  5. Han Reichgelt: Knowledge representation: An al perspective.Xabier Arrazola - 1993 - Theoria: Revista de Teoría, Historia y Fundamentos de la Ciencia 8 (19):187-188.
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  6. What the <0.70, 1.17, 0.99, 1.07> is a Symbol?Istvan S. 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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  7. 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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  8. 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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  9. 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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  10. 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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  11. Inference and Computational Semantics.Patrick Blackburn & Michael Kohlhase - 2004 - Journal of Logic, Language and Information 13 (2):117-120.
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  12. Davidson's Semantics and Computational Understanding of Language.Damjan Bojadžiev - 1989 - Grazer Philosophische Studien 36:133-139.
    Evaluating the usefulness of Davidson's semantics to computational understanding of language requires an examination of the role of a theory of truth in characterizing sentence meaning and logical form, and in particular of the connection between meaning and belief. The suggested conclusion is that the relevance of Davidson's semantics for computational semantics lies not so much in its methods and particular proposals of logical form as in its general orientation towards "desubstantializing" meaning.
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  13. 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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  14. Proceedings of the International Conference on Computational Semantics 9.J. Bos & S. Pulman (eds.) - 2011
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  15. 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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  16. Computational Semantics: An Introduction to Artificial Intelligence and Natural Language Comprehension.Eugene Charniak & Yorick Wilks (eds.) - 1976 - Distributors for the U.S.A. And Canada, Elsevier/North Holland.
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  17. Perceptual Symbols and Taxonomy Comparison.Xiang Chen - 2001 - Philosophy of Science 3 (September):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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  18. 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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  19. On the Representation of Linguistic Information.M. Teresa Espinal - 1992 - In Jes Ezquerro (ed.), Cognition, Semantics and Philosophy. Kluwer Academic Publishers. pp. 75--105.
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  20. 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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  21. In Reply to Philip Johnson-Laird's What's Wrong with Grandma's Guide to Procedural Semantics: A Reply to Jerry Fodor.Jerry A. Fodor - 1979 - Cognition 7 (March):93-95.
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  22. Tom Swift and His Procedural Grandmother.Jerry A. Fodor - 1978 - Cognition 6 (September):229-47.
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  23. 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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  24. Icon and Symbol.Michael J. Giordano - 1981 - Semiotics:29-37.
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  25. 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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  26. 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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  27. Eric Winsberg: Science in the Age of Computer Simulation. [REVIEW]Stefan Gruner - 2013 - Minds and Machines 23 (2):251-254.
  28. Truth Conditions and Procedural Semantics.Robert F. Hadley - 1990 - In Philip P. Hanson (ed.), Information, Language and Cognition. University of British Columbia Press.
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  29. Information, Language and Cognition.Philip P. Hanson (ed.) - 1990 - University of British Columbia Press.
  30. Commonsense Metaphysics and Lexical Semantics.Jerry R. Hobbs, William Croft, Todd Davies, Douglas Edwards & Kenneth Laws - 1987 - Computational Linguistics 13 (3&4):241-250.
    In the TACITUS project for using commonsense knowledge in the understanding of texts about mechanical devices and their failures, we have been developing various commonsense theories that are needed to mediate between the way we talk about the behavior of such devices and causal models of their operation. Of central importance in this effort is the axiomatization of what might be called commonsense metaphysics. This includes a number of areas that figure in virtually every domain of discourse, such as granularity, (...)
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  31. What's Wrong with Grandma's Guide to Procedural Semantics: A Reply to Jerry Fodor.Philip N. Johnson-Laird - 1978 - Cognition 9 (September):249-61.
  32. Procedural Semantics.Philip N. Johnson-Laird - 1977 - Cognition 5 (3):189-214.
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  33. Elements of Discourse Understanding.A. Joshi, Bruce H. Weber & Ivan A. Sag (eds.) - 1981 - Cambridge University Press.
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  34. 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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  35. Strategies for Natural Language Processing.Wendy G. Lehnert & Martin H. Ringle (eds.) - 1982 - Lawrence Erlbaum.
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  36. 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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  37. 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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  38. 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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  39. 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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  40. Natural Language Understanding Within a Cognitive Semantics Framework.Inger Lytje - 1989 - AI and Society 4 (4):276-290.
    The article argues that cognitive linguistic theory may prove an alternative to the Montague paradigm for designing natural language understanding systems. Within this framework it describes a system which models language understanding as a dialogical process between user and computer. The system operates with natural language texts as input and represent language meaning as entity-relationship diagrams.
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  41. 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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  42. Foundations of Statistical Natural Language Processing.Christopher Manning & Hinrich Schütze - 1999 - MIT Press.
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  43. Tarskian Semantics, or No Notation Without Denotation.Drew McDermott - 1978 - Cognitive Science 2 (3):277-82.
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  44. 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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  45. Introduction to a Systemic Theory of Meaning (July 2014 Update).Christophe Menant - 2014 - Dissertation,
    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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  46. Computation on Information, Meaning and Representations. An Evolutionary Approach (2011).Christophe Menant - 2011 - World Scientific.
    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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  47. Information and Meaning in Life, Humans and Robots (2005).Christophe Menant - 2005 - Dissertation, Paris Foundations of Information Sciences
    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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  48. 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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  49. 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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  50. 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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