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  1. The rational analysis of mind and behavior.Nick Chater & Mike Oaksford - 2000 - Synthese 122 (1-2):93-131.
    Rational analysis (Anderson 1990, 1991a) is an empiricalprogram of attempting to explain why the cognitive system isadaptive, with respect to its goals and the structure of itsenvironment. We argue that rational analysis has two importantimplications for philosophical debate concerning rationality. First,rational analysis provides a model for the relationship betweenformal principles of rationality (such as probability or decisiontheory) and everyday rationality, in the sense of successfulthought and action in daily life. Second, applying the program ofrational analysis to research on human reasoning (...)
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  • Rational and mechanistic perspectives on reinforcement learning.Nick Chater - 2009 - Cognition 113 (3):350-364.
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  • Open-world probabilistic databases: Semantics, algorithms, complexity.İsmail İlkan Ceylan, Adnan Darwiche & Guy Van den Broeck - 2021 - Artificial Intelligence 295 (C):103474.
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  • Explanation–Question–Response dialogue: An argumentative tool for explainable AI.Federico Castagna, Peter McBurney & Simon Parsons - forthcoming - Argument and Computation:1-23.
    Advancements and deployments of AI-based systems, especially Deep Learning-driven generative language models, have accomplished impressive results over the past few years. Nevertheless, these remarkable achievements are intertwined with a related fear that such technologies might lead to a general relinquishing of our lives’s control to AIs. This concern, which also motivates the increasing interest in the eXplainable Artificial Intelligence (XAI) research field, is mostly caused by the opacity of the output of deep learning systems and the way that it is (...)
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  • Razão e irracionalidade na representação do conhecimento.Walter A. Carnielli & Mamede Lima Marques - 1991 - Trans/Form/Ação 14:165-177.
    How is it possible that beginning from the negation of rational thoughts one comes to produce knowledge? This problem, besides its intrinsic interest, acquires a great relevance when the representation of a knowledge is settled, for example, on data and automatic reasoning. Many treatment ways have been tried, as in the case of the non-monotonic logics; logics that intend to formalize an idea of reasoning by default, etc. These attempts are incomplete and are subject to failure. A possible solution would (...)
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  • Meeting of the Association for Symbolic Logic, Campinas, Brazil, 1985.Walter Carnielli - 1986 - Journal of Symbolic Logic 51 (4):1093 - 1103.
    This is the ASL report on the 7th Latin American Symposium on Mathematical Logic held in Campinas, SP, Brazil, from July 29- August 02, 1985.
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  • Modulated logics and flexible reasoning.Walter Carnielli & Maria Cláudia C. Grácio - 2008 - Logic and Logical Philosophy 17 (3):211-249.
    This paper studies a family of monotonic extensions of first-order logic which we call modulated logics, constructed by extending classical logic through generalized quantifiers called modulated quantifiers. This approach offers a new regard to what we call flexible reasoning. A uniform treatment of modulated logics is given here, obtaining some general results in model theory. Besides reviewing the “Logic of Ultrafilters”, which formalizes inductive assertions of the kind “almost all”, two new monotonic logical systems are proposed here, the “Logic of (...)
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  • A formal account of Socratic-style argumentation.Martin W. A. Caminada - 2008 - Journal of Applied Logic 6 (1):109-132.
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  • An argument game for stable semantics.Martin Caminada & Yining Wu - 2008 - Logic Journal of the IGPL 17 (1):77-90.
    In this paper, we present a discussion game for argumentation under stable semantics. Our work is inspired by Vreeswijk and Prakken, who have defined a similar game for preferred semantics. In the current paper, we restate Vreeswijk and Prakken's work using the approach of argument labellings and then show how it can be adjusted for stable semantics. The nature of the resulting argument game is somewhat unusual, since stable semantics does not satisfy the property of relevance.
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  • Is intractability of nonmonotonic reasoning a real drawback?Marco Cadoli, Francesco M. Donini & Marco Schaerf - 1996 - Artificial Intelligence 88 (1-2):215-251.
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  • Gelfond–Zhang aggregates as propositional formulas.Pedro Cabalar, Jorge Fandinno, Torsten Schaub & Sebastian Schellhorn - 2019 - Artificial Intelligence 274 (C):26-43.
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  • A reasoning method for a paraconsistent logic.Arthur Buchsbaum & Tarcisio Pequeno - 1993 - Studia Logica 52 (2):281 - 289.
    A proof method for automation of reasoning in a paraconsistent logic, the calculus C1* of da Costa, is presented. The method is analytical, using a specially designed tableau system. Actually two tableau systems were created. A first one, with a small number of rules in order to be mathematically convenient, is used to prove the soundness and the completeness of the method. The other one, which is equivalent to the former, is a system of derived rules designed to enhance computational (...)
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  • A logical expression of reasoning.Arthur Buchsbaum, Tarcisio Pequeno & Marcelino Pequeno - 2007 - Synthese 154 (3):431 - 466.
    A non-monotonic logic, the Logic of Plausible Reasoning (LPR), capable of coping with the demands of what we call complex reasoning, is introduced. It is argued that creative complex reasoning is the way of reasoning required in many instances of scientific thought, professional practice and common life decision taking. For managing the simultaneous consideration of multiple scenarios inherent in these activities, two new modalities, weak and strong plausibility, are introduced as part of the Logic of Plausible Deduction (LPD), a deductive (...)
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  • Realistic desires.Jan Broersen, Mehdi Dastani & Leendert van der Torre - 2002 - Journal of Applied Non-Classical Logics 12 (2):287-308.
    Realism for agents with unconditional beliefs, desires and intentions has been analyzed in modal logic. This paper provides a logical analysis of realism for agents with conditional beliefs and desires in a rule based approach analogous to Reiter's default logic. We distinguish two types of realism, which we call ‘a priori' and ‘a posteriori' realism. We analyze whether these two new properties are compatible with other properties discussed in the literature, such as existence of extensions. We show that Reiter's default (...)
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  • Rectifying the Mischaracterization of Logic by Mental Model Theorists.Selmer Bringsjord & Naveen Sundar Govindarajulu - 2020 - Cognitive Science 44 (12):e12898.
    Khemlani et al. (2018) mischaracterize logic in the course of seeking to show that mental model theory (MMT) can accommodate a form of inference (, let us label it) they find in a high percentage of their subjects. We reveal their mischaracterization and, in so doing, lay a landscape for future modeling by cognitive scientists who may wonder whether human reasoning is consistent with, or perhaps even capturable by, reasoning in a logic or family thereof. Along the way, we note (...)
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  • Logic and artificial intelligence: Divorced, still married, separated ...? [REVIEW]Selmer Bringsjord & David A. Ferrucci - 1998 - Minds and Machines 8 (2):273-308.
    Though it''s difficult to agree on the exact date of their union, logic and artificial intelligence (AI) were married by the late 1950s, and, at least during their honeymoon, were happily united. What connubial permutation do logic and AI find themselves in now? Are they still (happily) married? Are they divorced? Or are they only separated, both still keeping alive the promise of a future in which the old magic is rekindled? This paper is an attempt to answer these questions (...)
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  • Strong inconsistency.Gerhard Brewka, Matthias Thimm & Markus Ulbricht - 2019 - Artificial Intelligence 267 (C):78-117.
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  • Representing meta-knowledge in Poole-systems.Gerhard Brewka - 2001 - Studia Logica 67 (2):153-165.
    We show how Poole-systems, a simple approach to nonmonotonic reasoning, can be extended to take meta-information into account adequately. The meta-information is used to guide the choice of formulas accepted by the reasoner as premises. Existence of a consistent set of conclusions is guaranteed by a least fixpoint construction. The proposed formalism has useful applications in defeasible reasoning, knowledge base fusion and belief revision.
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  • Qualitative choice logic.Gerhard Brewka, Salem Benferhat & Daniel Le Berre - 2004 - Artificial Intelligence 157 (1-2):203-237.
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  • Cumulative default logic.Gerhard Brewka - 1991 - Artificial Intelligence 50 (2):183-205.
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  • On decision-theoretic foundations for defaults.Ronen I. Brafman & Nir Friedman - 2001 - Artificial Intelligence 133 (1-2):1-33.
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  • An Overview of the KL‐ONE Knowledge Representation System.Ronald J. Brachman & James G. Schmolze - 1985 - Cognitive Science 9 (2):171-216.
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  • Vers la construction de descriptions argumentées d’un accident de la route : analyse de diverses stratégies argumentatives.Sara Boutouhami & Daniel Kayser - 2008 - Corela. Cognition, Représentation, Langage 6 (1).
    L'argumentation se situe à la croisée de disciplines diverses : pragmatique de l’énonciation, rhétorique, logique formelle, compréhension des textes. Elle constitue un champ d'études à la fois descriptif et critique qui s'intéresse à la mise en forme des arguments en vue, notamment, de la persuasion d'un auditoire. Nous nous intéressons ici à l’argumentation exprimée dans un cadre naturel, avec pour objectif la production de « descriptions argumentées », c’est-à-dire de descriptions qui, sans travestir la réalité, déclenchent chez le lecteur des (...)
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  • Unifying default reasoning and belief revision in a modal framework.Craig Boutilier - 1994 - Artificial Intelligence 68 (1):33-85.
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  • Épistémologie et intelligence artificielle.Yves Bouchard - 2020 - In André Lacroix (ed.), La philosophie pratique. Les Presses de l’Université de Laval. pp. 109-127.
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  • Explaining default intuitions using maximum entropy.Rachel A. Bourne - 2003 - Journal of Applied Logic 1 (3-4):255-271.
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  • Artificial nonmonotonic neural networks.B. Boutsinas & M. N. Vrahatis - 2001 - Artificial Intelligence 132 (1):1-38.
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  • Saturation, nonmonotonic reasoning and the closed-world assumption.Genevieve Bossu & Pierre Siegel - 1985 - Artificial Intelligence 25 (1):13-63.
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  • Belief Liberation.Richard Booth, Samir Chopra, Aditya Ghose & Thomas Meyer - 2005 - Studia Logica 79 (1):47-72.
    We provide a formal study of belief retraction operators that do not necessarily satisfy the postulate. Our intuition is that a rational description of belief change must do justice to cases in which dropping a belief can lead to the inclusion, or ‘liberation’, of others in an agent's corpus. We provide two models of liberation via retraction operators: ρ-liberation and linear liberation. We show that the class of ρ-liberation operators is included in the class of linear ones and provide axiomatic (...)
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  • A note on the rational closure of knowledge bases with both positive and negative knowledge.R. Booth & J. B. Paris - 1998 - Journal of Logic, Language and Information 7 (2):165-190.
    The notion of the rational closure of a positive knowledge base K of conditional assertions θ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document} $$i$$ \end{document} |∼ φ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document} $$i$$ \end{document} (standing for if θ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document} $$i$$ \end{document} then normally φ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document} $$i$$ \end{document}) was first introduced by Lehmann (1989) and developed by Lehmann and Magidor (...)
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  • Reasoning with infinite stable models.Piero A. Bonatti - 2004 - Artificial Intelligence 156 (1):75-111.
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  • On the logical properties of the nonmonotonic description logic DL N.P. A. Bonatti & L. Sauro - 2017 - Artificial Intelligence 248 (C):85-111.
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  • Free choice reasons.Daniel Bonevac - 2019 - Synthese 196 (2):735-760.
    I extend theories of nonmonotonic reasoning to account for reasons allowing free choice. My approach works with a wide variety of approaches to nonmonotonic reasoning and explains the connection between reasons for kinds of action and reasons for actions or subkinds falling under them. I use an Anderson–Kanger reduction of reason statements, identifying key principles in the logic of reasons.
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  • Defaulting on Reasons.Daniel Bonevac - 2018 - Noûs:229-259.
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  • A logic of non-monotonic interactions.Giovanni Boniolo, Marcello DʼAgostino, Mario Piazza & Gabriele Pulcini - 2013 - Journal of Applied Logic 11 (1):52-62.
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  • An abstract, argumentation-theoretic approach to default reasoning.A. Bondarenko, P. M. Dung, R. A. Kowalski & F. Toni - 1997 - Artificial Intelligence 93 (1-2):63-101.
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  • Safe inductions and their applications in knowledge representation.Bart Bogaerts, Joost Vennekens & Marc Denecker - 2018 - Artificial Intelligence 259 (C):167-185.
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  • Grounded fixpoints and their applications in knowledge representation.Bart Bogaerts, Joost Vennekens & Marc Denecker - 2015 - Artificial Intelligence 224 (C):51-71.
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  • Fixpoint semantics for active integrity constraints.Bart Bogaerts & Luís Cruz-Filipe - 2018 - Artificial Intelligence 255 (C):43-70.
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  • Local logics, non-monotonicity and defeasible argumentation.Gustavo A. Bodanza & Fernando A. Tohmé - 2004 - Journal of Logic, Language and Information 14 (1):1-12.
    In this paper we present an embedding of abstract argumentation systems into the framework of Barwise and Seligmans logic of information flow. We show that, taking P.M. Dungs characterization of argument systems, a local logic over states of a deliberation may be constructed. In this structure, the key feature of non-monotonicity of commonsense reasoning obtains as the transition from one local logic to another, due to a change in certain background conditions. Each of Dungs extensions of argument systems leads to (...)
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  • On the relation between default and modal nonmonotonic reasoning.Alexander Bochman - 1998 - Artificial Intelligence 101 (1-2):1-34.
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  • A causal approach to nonmonotonic reasoning.Alexander Bochman - 2004 - Artificial Intelligence 160 (1-2):105-143.
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  • Proof complexity of propositional default logic.Olaf Beyersdorff, Arne Meier, Sebastian Müller, Michael Thomas & Heribert Vollmer - 2011 - Archive for Mathematical Logic 50 (7-8):727-742.
    Default logic is one of the most popular and successful formalisms for non-monotonic reasoning. In 2002, Bonatti and Olivetti introduced several sequent calculi for credulous and skeptical reasoning in propositional default logic. In this paper we examine these calculi from a proof-complexity perspective. In particular, we show that the calculus for credulous reasoning obeys almost the same bounds on the proof size as Gentzen’s system LK. Hence proving lower bounds for credulous reasoning will be as hard as proving lower bounds (...)
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  • Yet some more complexity results for default logic.Rachel Ben-Eliyahu-Zohary - 2002 - Artificial Intelligence 139 (1):1-20.
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  • Reasoning with minimal models: efficient algorithms and applications.Rachel Ben-Eliyahu-Zohary & Luigi Palopoli - 1997 - Artificial Intelligence 96 (2):421-449.
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  • Default reasoning using classical logic.Rachel Ben-Eliyahu & Rina Dechter - 1996 - Artificial Intelligence 84 (1-2):113-150.
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  • Belief functions and default reasoning.Salem Benferhat, Alessandro Saffiotti & Philippe Smets - 2000 - Artificial Intelligence 122 (1--2):1--69.
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  • An Overview of Possibilistic Handling of Default Reasoning, with Experimental Studies.Salem Benferhat, Jean F. Bonnefon & Rui da Silva Neves - 2005 - Synthese 146 (1-2):53-70.
    This paper first provides a brief survey of a possibilistic handling of default rules. A set of default rules of the form, "generally, from α deduce β", is viewed as the family of possibility distributions satisfying constraints expressing that the situation where α and β is true has a greater plausibility than the one where α and ⇁β is true. When considering only the subset of linear possibility distributions, the well-known System P of postulates proposed by Kraus, Lehmann and Magidor, (...)
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  • A modal logic for subjective default reasoning.Shai Ben-David & Rachel Ben-Eliyahu-Zohary - 2000 - Artificial Intelligence 116 (1-2):217-236.
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  • Argument in artificial intelligence and law.Trevor Bench-Capon - 1997 - Artificial Intelligence and Law 5 (4):249-261.
    In this paper I shall discuss the notion of argument, and the importanceof argument in AI and Law. I shall distinguish four areas where argument hasbeen applied: in modelling legal reasoning based on cases; in thepresentation and explanation of results from a rule based legal informationsystem; in the resolution of normative conflict and problems ofnon-monotonicity; and as a basis for dialogue games to support the modellingof the process of argument. The study of argument is held to offer prospectsof real progress (...)
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