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  1. Mundane reasoning by settling on a plausible model.Mark Derthick - 1990 - Artificial Intelligence 46 (1-2):107-157.
  • Modeling agents as qualitative decision makers.Ronen I. Brafman & Moshe Tennenholtz - 1997 - Artificial Intelligence 94 (1-2):217-268.
  • Reasoning with minimal models: efficient algorithms and applications.Rachel Ben-Eliyahu-Zohary & Luigi Palopoli - 1997 - Artificial Intelligence 96 (2):421-449.
  • Base-rate respect: From ecological rationality to dual processes.Aron K. Barbey & Steven A. Sloman - 2007 - Behavioral and Brain Sciences 30 (3):241-254.
    The phenomenon of base-rate neglect has elicited much debate. One arena of debate concerns how people make judgments under conditions of uncertainty. Another more controversial arena concerns human rationality. In this target article, we attempt to unpack the perspectives in the literature on both kinds of issues and evaluate their ability to explain existing data and their conceptual coherence. From this evaluation we conclude that the best account of the data should be framed in terms of a dual-process model of (...)
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  • Mental imagery: In search of a theory.Zenon W. Pylyshyn - 2002 - Behavioral and Brain Sciences 25 (2):157-182.
    It is generally accepted that there is something special about reasoning by using mental images. The question of how it is special, however, has never been satisfactorily spelled out, despite more than thirty years of research in the post-behaviorist tradition. This article considers some of the general motivation for the assumption that entertaining mental images involves inspecting a picture-like object. It sets out a distinction between phenomena attributable to the nature of mind to what is called the cognitive architecture, and (...)
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  • Objective Evaluation of Demonstrative Arguments.Emmanuel Trouche, Jing Shao & Hugo Mercier - 2019 - Argumentation 33 (1):23-43.
    Many experiments suggest that participants are more critical of arguments that challenge their views or that come from untrustworthy sources. However, other results suggest that this might not be true of demonstrative arguments. A series of four experiments tested whether people are influenced by two factors when they evaluate demonstrative arguments: how confident they are in the answer being challenged by the argument, and how much they trust the source of the argument. Participants were not affected by their confidence in (...)
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  • Model-preference default theories.Bart Selman & Henry A. Kautz - 1990 - Artificial Intelligence 45 (3):287-322.
  • Rationality and intelligence.Stuart J. Russell - 1997 - Artificial Intelligence 94 (1-2):57-77.
  • Semiotic schemas: A framework for grounding language in action and perception.Deb Roy - 2005 - Artificial Intelligence 167 (1-2):170-205.
  • On the hardness of approximate reasoning.Dan Roth - 1996 - Artificial Intelligence 82 (1-2):273-302.
  • A Plea for Ecological Argument Technologies.Fabio Paglieri - 2017 - Philosophy and Technology 30 (2):209-238.
    In spite of significant research efforts, argument technologies do not seem poised to scale up as much as most commentators would hope or even predict. In this paper, I discuss what obstacles bar the way to more widespread success of argument technologies and venture some suggestions on how to circumvent such difficulties: doing so will require a significant shift in how this research area is typically understood and practiced. I begin by exploring a much broader yet closely related question: To (...)
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  • The logic of natural sampling.David E. Over - 2007 - Behavioral and Brain Sciences 30 (3):277-277.
    Barbey & Sloman (B&S) relegate the logical rule of the excluded middle to a footnote. But this logical rule is necessary for natural sampling. Making the rule explicit in a logical tree can make a problem easier to solve. Examples are given of uses of the rule that are non-constructive and not reducible to a domain-specific module.
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  • Computational complexity of terminological reasoning in BACK.Bernhard Nebel - 1988 - Artificial Intelligence 34 (3):371-383.
  • Hybrid reasoning using universal attachment.Karen L. Myers - 1994 - Artificial Intelligence 67 (2):329-375.
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  • Building large knowledge-based systems: Representation and inference in the cyc project.Drew McDermott - 1993 - Artificial Intelligence 61 (1):53-63.
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  • On our Best Behaviour.Hector J. Levesque - 2014 - Artificial Intelligence 213 (C):27-35.
  • Logic and the complexity of reasoning.Hector J. Levesque - 1988 - Journal of Philosophical Logic 17 (4):355 - 389.
  • Limited reasoning in first-order knowledge bases.Gerhard Lakemeyer - 1994 - Artificial Intelligence 71 (2):213-255.
  • Reasoning with models.Roni Khardon & Dan Roth - 1996 - Artificial Intelligence 87 (1-2):187-213.
  • Defaults and relevance in model-based reasoning.Roni Khardon & Dan Roth - 1997 - Artificial Intelligence 97 (1-2):169-193.
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  • Hard problems for simple default logics.Henry A. Kautz & Bart Selman - 1991 - Artificial Intelligence 49 (1-3):243-279.
  • Peirce, logic diagrams, and the elementary operations of reasoning.P. N. Johnson-Laird - 2002 - Thinking and Reasoning 8 (1):69 – 95.
    This paper describes Peirce's systems of logic diagrams, focusing on the so-called ''existential'' graphs, which are equivalent to the first-order predicate calculus. It analyses their implications for the nature of mental representations, particularly mental models with which they have many characteristics in common. The graphs are intended to be iconic, i.e., to have a structure analogous to the structure of what they represent. They have emergent logical consequences and a single graph can capture all the different ways in which a (...)
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  • The Manipulation of Images to Handle Indeterminacy in Spatial Reasoning.Thomas R. Ioerger - 1994 - Cognitive Science 18 (4):551-593.
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  • The many uses of 'belief' in AI.Robert F. Hadley - 1991 - Minds and Machines 1 (1):55-74.
    Within AI and the cognitively related disciplines, there exist a multiplicity of uses of belief. On the face of it, these differing uses reflect differing views about the nature of an objective phenomenon called belief. In this paper I distinguish six distinct ways in which belief is used in AI. I shall argue that not all these uses reflect a difference of opinion about an objective feature of reality. Rather, in some cases, the differing uses reflect differing concerns with special (...)
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  • Re: CycLing paper reviews.R. V. Guha & Douglas B. Lenat - 1993 - Artificial Intelligence 61 (1):149-174.
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  • PALO: a probabilistic hill-climbing algorithm.Russell Greiner - 1996 - Artificial Intelligence 84 (1-2):177-208.
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  • The Perceived Objectivity of Ethical Beliefs: Psychological Findings and Implications for Public Policy. [REVIEW]Geoffrey P. Goodwin & John M. Darley - 2010 - Review of Philosophy and Psychology 1 (2):161-188.
    Ethical disputes arise over differences in the content of the ethical beliefs people hold on either side of an issue. One person may believe that it is wrong to have an abortion for financial reasons, whereas another may believe it to be permissible. But, the magnitude and difficulty of such disputes may also depend on other properties of the ethical beliefs in question—in particular, how objective they are perceived to be. As a psychological property of moral belief, objectivity is relatively (...)
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  • The role of representation in bayesian reasoning: Correcting common misconceptions.Gerd Gigerenzer & Ulrich Hoffrage - 2007 - Behavioral and Brain Sciences 30 (3):264-267.
    The terms nested sets, partitive frequencies, inside-outside view, and dual processes add little but confusion to our original analysis (Gigerenzer & Hoffrage 1995; 1999). The idea of nested set was introduced because of an oversight; it simply rephrases two of our equations. Representation in terms of chances, in contrast, is a novel contribution yet consistent with our computational analysis System 1.dual process theory” is: Unless the two processes are defined, this distinction can account post hoc for almost everything. In contrast, (...)
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  • Knowledge in and out of Contrast.Mikkel Gerken & James R. Beebe - 2014 - Noûs 50 (1):133-164.
    We report and discuss the results of a series of experiments that address a contrast effect exhibited by folk judgments about knowledge ascriptions. The contrast effect, which was first reported by Schaffer and Knobe, is an important aspect of our folk epistemology. However, there are competing theoretical accounts of it. We shed light on the various accounts by providing novel empirical data and theoretical considerations. Our key findings are, firstly, that belief ascriptions exhibit a similar contrast effect and, secondly, that (...)
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  • The brain attics: the strategic role of memory in single and multi-agent inquiry.Emmanuel J. Genot & Justine Jacot - 2020 - Synthese 197 (3):1203-1224.
    M. B. Hintikka and J. Hintikka claimed that their reconstruction of the ‘Sherlock Holmes sense of deduction’ can “serve as an explication for the link between intelligence and memory”. The claim is vindicated, first for the single-agent case, where the reconstruction captures strategies for accessing the content of a distributed and associative memory; then, for the multi-agent case, where the reconstruction captures strategies for accessing knowledge distributed in a community. Moreover, the reconstruction of the ‘Sherlock Holmes sense of deduction’ allows (...)
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  • Two theses of knowledge representation: Language restrictions, taxonomic classification, and the utility of representation services.Jon Doyle & Ramesh S. Patil - 1991 - Artificial Intelligence 48 (3):261-297.
  • Impediments to universal preference-based default theories.Jon Doyle & Michael P. Wellman - 1991 - Artificial Intelligence 49 (1-3):97-128.
  • Mental imagery.Nigel J. T. Thomas - 2001 - Stanford Encyclopedia of Philosophy.
    Mental imagery (varieties of which are sometimes colloquially refered to as “visualizing,” “seeing in the mind's eye,” “hearing in the head,” “imagining the feel of,” etc.) is quasi-perceptual experience; it resembles perceptual experience, but occurs in the absence of the appropriate external stimuli. It is also generally understood to bear intentionality (i.e., mental images are always images of something or other), and thereby to function as a form of mental representation. Traditionally, visual mental imagery, the most discussed variety, was thought (...)
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  • S eeingand visualizing: I T' S n otwhaty ou T hink.Zenon Pylyshyn - unknown
    6. Seeing With the Mind’s Eye 1: The Puzzle of Mental Imagery .................................................6-1 6.1 What is the puzzle about mental imagery?..............................................................................6-1 6.2 Content, form and substance of representations ......................................................................6-6 6.3 What is responsible for the pattern of results obtained in imagery studies?.................................6-8..
     
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