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  1. William J. Rapaport, Erwin M. Segal, Stuart C. Shapiro, David A. Zubin, Gail A. Bruder, Judith Felson Duchan & David M. Mark, Cognitive and Computer Systems for Understanding Narrative Text.
    This project continues our interdisciplinary research into computational and cognitive aspects of narrative comprehension. Our ultimate goal is the development of a computational theory of how humans understand narrative texts. The theory will be informed by joint research from the viewpoints of linguistics, cognitive psychology, the study of language acquisition, literary theory, geography, philosophy, and artificial intelligence. The linguists, literary theorists, and geographers in our group are developing theories of narrative language and spatial understanding that are being tested by the (...)
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  2. Stuart C. Shapiro & William J. Rapaport (1992). The SNePS Family. Computers and Mathematics with Applications 23:243-275.
    SNePS, the Semantic Network Processing System 45, 54], has been designed to be a system for representing the beliefs of a natural-language-using intelligent system (a \cognitive agent"). It has always been the intention that a SNePS-based \knowledge base" would ultimatelybe built, not by a programmeror knowledge engineer entering representations of knowledge in some formallanguage or data entry system, but by a human informing it using a natural language (NL) (generally supposed to be English), or by the system reading books or (...)
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  3. William J. Rapaport, Stuart C. Shapiro & Janyce M. Wiebe (1997). Quasi‐Indexicals and Knowledge Reports. Cognitive Science 21 (1):63-107.
    We present a computational analysis of de re, de dicto, and de se belief and knowledge reports. Our analysis solves a problem first observed by Hector-Neri Castañeda, namely, that the simple rule -/- `(A knows that P) implies P' -/- apparently does not hold if P contains a quasi-indexical. We present a single rule, in the context of a knowledge-representation and reasoning system, that holds for all P, including those containing quasi-indexicals. In so doing, we explore the difference between reasoning (...)
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  4. Stuart C. Shapiro & William J. Rapaport (1991). Models and Minds. In Robert E. Cummins & John L. Pollock (eds.), Philosophy and AI. Cambridge: MIT Press 215--259.
    Cognitive agents, whether human or computer, that engage in natural-language discourse and that have beliefs about the beliefs of other cognitive agents must be able to represent objects the way they believe them to be and the way they believe others believe them to be. They must be able to represent other cognitive agents both as objects of beliefs and as agents of beliefs. They must be able to represent their own beliefs, and they must be able to represent beliefs (...)
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  5.  5
    Anthony S. Maida & Stuart C. Shapiro (1982). Intensional Concepts in Propositional Semantic Networks. Cognitive Science 6 (4):291-330.
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  6.  17
    Stuart C. Shapiro & Jonathan P. Bona (2010). The Glair Cognitive Architecture. International Journal of Machine Consciousness 2 (2):307-332.
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  7.  45
    Stuart C. Shapiro (1995). Computationalism. Minds and Machines 5 (4):467-87.
    Computationalism, the notion that cognition is computation, is a working hypothesis of many AI researchers and Cognitive Scientists. Although it has not been proved, neither has it been disproved. In this paper, I give some refutations to some well-known alleged refutations of computationalism. My arguments have two themes: people are more limited than is often recognized in these debates; computer systems are more complicated than is often recognized in these debates. To underline the latter point, I sketch the design and (...)
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  8.  23
    Hans Chalupsky & Stuart C. Shapiro (1994). SL: A Subjective, Intensional Logic of Belief. In Ashwin Ram & Kurt Eiselt (eds.), Proceedings of the Sixteenth Annual Conference of the Cognitive Science Society. Erlbaum 165--170.
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  9.  13
    Stuart C. Shapiro (2002). Whose Norm? Trends in Cognitive Sciences 6 (11):490.
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  10.  12
    Stuart C. Shapiro (1993). Preface. Minds and Machines 3 (4):377-380.
  11.  32
    Syed S. Ali & Stuart C. Shapiro (1993). Natural Language Processing Using a Propositional Semantic Network with Structured Variables. Minds and Machines 3 (4):421-451.
    We describe a knowledge representation and inference formalism, based on an intensional propositional semantic network, in which variables are structures terms consisting of quantifier, type, and other information. This has three important consequences for natural language processing. First, this leads to an extended, more natural formalism whose use and representations are consistent with the use of variables in natural language in two ways: the structure of representations mirrors the structure of the language and allows re-use phenomena such as pronouns and (...)
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  12.  27
    David L. Kemmerer, Kenneth Aizawa, Donald H. Berman, Stacey L. Edgar, James E. Tomberlin, J. Christopher Maloney, John L. Bell, Stuart C. Shapiro, Georges Rey, Morton L. Schagrin, Robert A. Wilson & Patrick J. Hayes (1995). Book Reviews. [REVIEW] Minds and Machines 5 (3):411-465.
  13.  8
    Stuart C. Shapiro (2003). Knowledge Representation. In L. Nadel (ed.), Encyclopedia of Cognitive Science. Nature Publishing Group
  14.  6
    Stuart C. Shapiro & Bell Hall (1993). Knowledge Representation for Natural Language Processing. Minds and Machines 3 (4):377-380.
  15.  1
    William J. Rapaport, Joao P. Martins & Stuart C. Shapiro (1988). Theoretical Foundations for Belief Revision. Journal of Symbolic Logic 53 (2):669.
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  16. Debra T. Burhans & Stuart C. Shapiro (2007). Defining Answer Classes Using Resolution Refutation. Journal of Applied Logic 5 (1):70-91.
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