Search results for 'defeasible reasoning' (try it on Scholar)

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  1. Fernando Tohmé, Claudio Delrieux & Otávio Bueno (2011). Defeasible Reasoning + Partial Models: A Formal Framework for the Methodology of Research Programs. [REVIEW] Foundations of Science 16 (1):47-65.score: 240.0
    In this paper we show that any reasoning process in which conclusions can be both fallible and corrigible can be formalized in terms of two approaches: (i) syntactically, with the use of defeasible reasoning, according to which reasoning consists in the construction and assessment of arguments for and against a given claim, and (ii) semantically, with the use of partial structures, which allow for the representation of less than conclusive information. We are particularly interested in the (...)
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  2. Robert L. Causey (1991). The Epistemic Basis of Defeasible Reasoning. Minds and Machines 1 (4):437-458.score: 240.0
    This article argues that: (i) Defeasible reasoning is the use of distinctive procedures for belief revision when new evidence or new authoritative judgment is interpolated into a system of beliefs about an application domain. (ii) These procedures can be explicated and implemented using standard higher-order logic combined with epistemic assumptions about the system of beliefs. The procedures mentioned in (i) depend on the explication in (ii), which is largely described in terms of a Prolog program, EVID, which implements (...)
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  3. Robert L. Causey (2003). Computational Dialogic Defeasible Reasoning. Argumentation 17 (4):421-450.score: 240.0
    This article begins with an introduction to defeasible (nonmonotonic) reasoning and a brief description of a computer program, EVID, which can perform such reasoning. I then explain, and illustrate with examples, how this program can be applied in computational representations of ordinary dialogic argumentation. The program represents the beliefs and doubts of the dialoguers, and uses these propositional attitudes, which can include commonsense defeasible inference rules, to infer various changing conclusions as a dialogue progresses. It is (...)
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  4. Timothy R. Colburn (1991). Defeasible Reasoning and Logic Programming. Minds and Machines 1 (4):417-436.score: 240.0
    The general conditions of epistemic defeat are naturally represented through the interplay of two distinct kinds of entailment, deductive and defeasible. Many of the current approaches to modeling defeasible reasoning seek to define defeasible entailment via model-theoretic notions like truth and satisfiability, which, I argue, fails to capture this fundamental distinction between truthpreserving and justification-preserving entailments. I present an alternative account of defeasible entailment and show how logic programming offers a paradigm in which the (...)
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  5. Timothy R. Colburn (1991). Program Verification, Defeasible Reasoning, and Two Views of Computer Science. Minds and Machines 1 (1):97-116.score: 234.0
    In this paper I attempt to cast the current program verification debate within a more general perspective on the methodologies and goals of computer science. I show, first, how any method involved in demonstrating the correctness of a physically executing computer program, whether by testing or formal verification, involves reasoning that is defeasible in nature. Then, through a delineation of the senses in which programs can be run as tests, I show that the activities of testing and formal (...)
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  6. Robert C. Koons (2001). Defeasible Reasoning, Special Pleading and the Cosmological Argument: A Reply to Oppy. Faith and Philosophy 18 (2):192-203.score: 222.0
    This is a reply to a paper by Graham Oppy in the July, 1999 issue of this journal, “Koons’ Cosmological Argument.” Recent work in defeasible or nonmonotonic logic means that the cosmological argument can be cast in such a way that it does not presuppose that every contingent situation, without exception, has a cause. Instead, the burden of proof is shifted to the skeptic, who must produce positive reasons for thinking that the cosmos is an exception to the (...) law of causality. I show how Oppy’s critique can be turned into a plausible rebuttal of my argument. However, this rebuttal can be set aside when the original argument is supplemented by a plausible account of the nature of causal priority. Several independent lines of argument in support of this account are outlined. (shrink)
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  7. Timothy R. Colburn (1995). Heuristics, Justification, and Defeasible Reasoning. Minds and Machines 5 (4):467-487.score: 216.0
    Heuristics can be regarded as justifying the actions and beliefs of problem-solving agents. I use an analysis of heuristics to argue that a symbiotic relationship exists between traditional epistemology and contemporary artificial intelligence. On one hand, the study of models of problem-solving agents usingquantitative heuristics, for example computer programs, can reveal insight into the understanding of human patterns of epistemic justification by evaluating these models'' performance against human problem-solving. On the other hand,qualitative heuristics embody the justifying ability of defeasible (...)
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  8. Katsumi Nitta & Masato Shibasaki (1997). Defeasible Reasoning in Japanese Criminal Jurisprudence. Artificial Intelligence and Law 5 (1-2):139-159.score: 216.0
    Modeling legal argumentation is one of the most important research in AI and Law, and a lot of models have been proposed. However, most research has not treated value judgement and debate. In this paper, we introduce a legal reasoning model which covers various aspects of legalreasoning such as making argument, selecting argument and debate.Furthermore, we present how criminal law is described and reasoned inthis model.
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  9. Gerard A. W. Vreeswijk (1995). The Computational Value of Debate in Defeasible Reasoning. Argumentation 9 (2):305-342.score: 192.0
    Defeasible reasoning is concerned with the logics of non-deductive argument. As is described in the literature, the study of this type of reasoning is considerably more involved than the study of deductive argument, even so that, in realistic applications, there is often a lack of resources to perform an exhaustive analysis. It follows that, in a theory of defeasible reasoning, the order and direction in which arguments are developed, i.e. theprocedure, is important. The aim of (...)
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  10. Jonathan Weisberg, Pollock's Theory of Defeasible Reasoning.score: 180.0
    An introduction to the motivations and mechanics of John Pollock's theory of defeasible reasoning, from a lecture at the Northern Institute of Philosophy in 2010.
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  11. Douglas Walton (2011). Defeasible Reasoning and Informal Fallacies. Synthese 179 (3):377 - 407.score: 180.0
    This paper argues that some traditional fallacies should be considered as reasonable arguments when used as part of a properly conducted dialog. It is shown that argumentation schemes, formal dialog models, and profiles of dialog are useful tools for studying properties of defeasible reasoning and fallacies. It is explained how defeasible reasoning of the most common sort can deteriorate into fallacious argumentation in some instances. Conditions are formulated that can be used as normative tools to judge (...)
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  12. Wolfgang Spohn (2002). A Brief Comparison of Pollock's Defeasible Reasoning and Ranking Functions. Synthese 131 (1):39-56.score: 180.0
    In this paper two theories of defeasible reasoning, Pollock's account and my theory of ranking functions, are compared, on a strategic level, since a strictly formal comparison would have been unfeasible. A brief summary of the accounts shows their basic difference: Pollock's is a strictly computational one, whereas ranking functions provide a regulative theory. Consequently, I argue that Pollock's theory is normatively defective, unable to provide a theoretical justification for its basic inference rules and thus an independent notion (...)
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  13. John Pollock, A Recursive Semantics for Defeasible Reasoning.score: 180.0
    One of the most striking characteristics of human beings is their ability to function successfully in complex environments about which they know very little. In light of our pervasive ignorance, we cannot get around in the world just reasoning deductively from our prior beliefs together with new perceptual input. As our conclusions are not guaranteed to be true, we must countenance the possibility that new information will lead us to change our minds, withdrawing previously adopted beliefs. In this sense, (...)
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  14. D. Gabbay & P. Smets (eds.) (1998). Handbook of Defeasible Reasoning and Uncertainty Management Systems, Vol 3. Kluwer Academic Pub.score: 180.0
    HANDBOOK OF DEFEASIBLE REASONING AND UNCERTAINTY MANAGEMENT SYSTEMS EDITORS: DOV M. ... and A. Hunter Volume 3: Belief Change Edited by D. Dubois and H. Prade HANDBOOK OF DEFEASIBLE REASONING AND ...
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  15. John Pollock, Oscar: An Agent Architecture Based on Defeasible Reasoning.score: 180.0
    Proceedings of the 2008 AAAI Spring Symposium on Architectures for Intelligent Theory-Based Agents. “OSCAR is a fully implemented architecture for a cognitive agent, based largely on the author’s work in philosophy concerning epistemology and practical cognition. The seminal idea is that a generally intelligent agent must be able to function in an environment in which it is ignorant of most matters of fact. The architecture incorporates a general-purpose defeasible reasoner, built on top of an efficient natural deduction reasoner for (...)
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  16. R. Loui (1990). Defeasible Reasoning About Utilities and Decision Trees. In Kyburg Henry E., Loui Ronald P. & Carlson Greg N. (eds.), Knowledge Representation and Defeasible Reasoning. Kluwer. 345--359.score: 180.0
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  17. John F. Horty (2001). Argument Construction and Reinstatement in Logics for Defeasible Reasoning. Artificial Intelligence and Law 9 (1):1-28.score: 174.0
    This paper points out some problems with two recent logical systems – one due to Prakken and Sartor, the other due to Kowalski and Toni – designedfor the representation of defeasible arguments in general, but with a specialemphasis on legal reasoning.
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  18. John Pollock (2001). ``Defeasible Reasoning with Variable Degrees of Justification&Quot. Artificial Intelligence 133:233-282.score: 162.0
    The question addressed in this paper is how the degree of justification of a belief is determined. A conclusion may be supported by several different arguments, the arguments typically being defeasible, and there may also be arguments of varying strengths for defeaters for some of the supporting arguments. What is sought is a way of computing the “on sum” degree of justification of a conclusion in terms of the degrees of justification of all relevant premises and the strengths of (...)
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  19. Robert C. Koons (2001). Defeasible Reasoning, Special Pleading and the Cosmological Argument. Faith and Philosophy 18 (2):192-203.score: 162.0
    This is a reply to a paper by Graham Oppy in the July, 1999 issue of this journal, “Koons’ Cosmological Argument.” Recent work in defeasible or nonmonotonic logic means that the cosmological argument can be cast in such a way that it does not presuppose that every contingent situation, without exception, has a cause. Instead, the burden of proof is shifted to the skeptic, who must produce positive reasons for thinking that the cosmos is an exception to the (...) law of causality. I show how Oppy’s critique can be turned into a plausible rebuttal of my argument. However, this rebuttal can be set aside when the original argument is supplemented by a plausible account of the nature of causal priority. Several independent lines of argument in support of this account are outlined. (shrink)
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  20. G. Aldo Antonelli (1996). Defeasible Reasoning as a Cognitive Model. In Krister Segerberg (ed.), The Parikh Project. Seven Papers in Honour of Rohit. Uppsala Prints & Preprints in Philosophy.score: 162.0
    One of the most important developments over the last twenty years both in logic and in Artificial Intelligence is the emergence of so-called non-monotonic logics. These logics were initially developed by McCarthy [10], McDermott & Doyle [13], and Reiter [17]. Part of the original motivation was to provide a formal framework within which to model cognitive phenomena such as defeasible inference and defeasible knowledge representation, i.e., to provide a formal account of the fact that reasoners can reach conclusions (...)
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  21. John Pollock (1987). Defeasible Reasoning. Cognitive Science 11 (4):481-518.score: 156.0
    There was a long tradition in philosophy according to which good reasoning had to be deductively valid. However, that tradition began to be questioned in the 1960’s, and is now thoroughly discredited. What caused its downfall was the recognition that many familiar kinds of reasoning are not deductively valid, but clearly confer justification on their conclusions. Here are some simple examples.
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  22. Stewart Cohen (2010). Bootstrapping, Defeasible Reasoning, and a Priori Justification. Philosophical Perspectives 24 (1):141-159.score: 150.0
  23. Robert C. Koons, Defeasible Reasoning. Stanford Encyclopedia of Philosophy.score: 150.0
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  24. Robert Alexy (2000). Henry Prakken (1997), Logical Tools for Modelling Legal Argument. A Study of Defeasible Reasoning in Law. Argumentation 14 (1):65-72.score: 150.0
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  25. Pollock † & L. John (2011). Defeasible Reasoning and Degrees of Justification. Argument and Computation 1 (1):7-22.score: 150.0
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  26. Jan Albert Laar (2013). J. Anthony Blair and Ralph H. Johnson (Eds): Conductive Argument: An Overlooked Type of Defeasible Reasoning. [REVIEW] Argumentation 27 (3):337-344.score: 150.0
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  27. R. P. Loui (1999). Review of H. Prakken, Logical Tools for Modelling Legal Argument. A Study of Defeasible Reasoning in Law. [REVIEW] Journal of Symbolic Logic 64 (4):1840-1841.score: 150.0
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  28. Fabio Paglieri (2013). Conductive Argument, An Overlooked Type of Defeasible Reasoning. Informal Logic 33 (3):438-461.score: 150.0
    Edited by J. Anthony Blair and Ralph H. Johnson King’s College London, UK: College Publications, 2011. Pp. vii, 1-299. Softcover. ISBN: 978-1-84890-030-1. US$ ~20.
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  29. L. M. M. Royakkers (2000). Henry Prakken, Logical Tools for Modelling Legal Argument: A Study of Defeasible Reasoning in Law. [REVIEW] Journal of Logic, Language and Information 9 (3):379-387.score: 150.0
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  30. Bart Verheij (2000). Henry Prakken (1997). Logical Tools for Modelling Legal Argument. A Study of Defeasible Reasoning in Law. Artificial Intelligence and Law 8 (1):35-65.score: 150.0
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  31. Jan Albert van Laar (2013). J. Anthony Blair and Ralph H. Johnson (Eds): Conductive Argument: An Overlooked Type of Defeasible Reasoning. Argumentation 27 (3):337-344.score: 150.0
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  32. Gerhard Schurz (1996). The Role or Negation in Nonmonotonio Loom and Defeasible Reasoning. In H. Wansing (ed.), Negation: A Notion in Focus. W. De Gruyter. 7--197.score: 150.0
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  33. John L. Pollock† (2010). Defeasible Reasoning and Degrees of Justification. Argument and Computation 1 (1):7-22.score: 150.0
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  34. Francesca Toni (2008). Assumption-Based Argumentation for Closed and Consistent Defeasible Reasoning. In. In Satoh (ed.), New Frontiers in Artificial Intelligence. Springer. 390--402.score: 150.0
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  35. Douglas Walton (2011). Reasoning About Knowledge Using Defeasible Logic. Argument and Computation 2 (2-3):131 - 155.score: 144.0
    In this paper, the Carneades argumentation system is extended to represent a procedural view of inquiry in which evidence is marshalled to support or defeat claims to knowledge. The model is a sequence of moves in a collaborative group inquiry in which parties take turns making assertions about what is known or not known, putting forward evidence to support them, and subjecting these moves to criticisms. It is shown how this model of evaluating evidence in an inquiry is based on (...)
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  36. Henry Prakken (1996). Two Approaches to the Formalisation of Defeasible Deontic Reasoning. Studia Logica 57 (1):73 - 90.score: 144.0
    This paper compares two ways of formalising defeasible deontic reasoning, both based on the view that the issues of conflicting obligations and moral dilemmas should be dealt with from the perspective of nonmonotonic reasoning. The first way is developing a special nonmonotonic logic for deontic statements. This method turns out to have some limitations, for which reason another approach is recommended, viz. combining an already existing nonmonotonic logic with a deontic logic. As an example of this method (...)
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  37. John Pollock (2011). Reasoning Defeasibly About Probabilities. Synthese 181 (2):317 - 352.score: 134.0
    In concrete applications of probability, statistical investigation gives us knowledge of some probabilities, but we generally want to know many others that are not directly revealed by our data. For instance, we may know prob(P/Q) (the probability of P given Q) and prob(P/R), but what we really want is prob(P/Q& R), and we may not have the data required to assess that directly. The probability calculus is of no help here. Given prob(P/Q) and prob(P/R), it is consistent with the probability (...)
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  38. Marvin Belzer (1986). Reasoning with Defeasible Principles. Synthese 66 (1):135 - 158.score: 120.0
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  39. Peter Verdée (2012). Modelling Def+Easible Reasoning by Means of Adaptive Logic Games. Logic Journal of the Igpl 20 (2):417-437.score: 120.0
    In this article, I present a dynamic logic game for defeasible reasoning. I argue that, as far as defeasible reasoning is concerned, one should distinguish between practical and ideal rationality. Starting from the adaptive logic framework, I formalize both rationality notions by means of logic games. The presented adaptive logic games are based on (i) standard logic games on the one hand and (ii) dynamic proof procedures for adaptive logic on the other hand. The games are (...)
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  40. G. Aldo Antonelli (2005). Grounded Consequence for Defeasible Logic. Cambridge University Press.score: 114.0
    This is a title on the foundations of defeasible logic, which explores the formal properties of everyday reasoning patterns whereby people jump to conclusions, reserving the right to retract them in the light of further information. Although technical in nature the book contains sections that outline basic issues by means of intuitive and simple examples. This book is primarily targeted at philosophers interested in the foundations of defeasible logic, logicians, and specialists in artificial intelligence and theoretical computer (...)
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  41. Frederick Maier & Donald Nute (2010). Well-Founded Semantics for Defeasible Logic. Synthese 176 (2):243 - 274.score: 114.0
    Fixpoint semantics are provided for ambiguity blocking and propagating variants of Nute's defeasible logic. The semantics are based upon the well-founded semantics for logic programs. It is shown that the logics are sound with respect to their counterpart semantics and complete for locally finite theories. Unlike some other nonmonotonic reasoning formalisms such as Reiter's default logic, the two defeasible logics are directly skeptical and so reject floating conclusions. For defeasible theories with transitive priorities on defeasible (...)
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  42. Matthew Lister (2014). Review of Defeasibility in Philosophy: Knowledge, Agency, Responsibility, and the Law; Claudia Blöser, Mikael Janvid, Hannes Ole Matthiessen, and Marcus Willaschek (Eds.). [REVIEW] Notre Dame Philosophical Reviews 2014.score: 100.0
    This volume is based on papers presented at a conference on defeasibility in ethics, epistemology, law, and logic that took place at the Goethe University in Frankfurt in 2010. The subtitle (“Knowledge, Agency, Responsibility, and the Law”) better reflects the content than does the title of the original conference. None of the papers focuses directly or primarily on defeasible reasoning in logic, though a few touch on this indirectly. Nor are the papers evenly split among the topics. Six (...)
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  43. Giuseppe Primiero (2012). A Contextual Type Theory with Judgemental Modalities for Reasoning From Open Assumptions. Logique and Analyse 220:579-600.score: 96.0
    Contextual type theories are largely explored in their applications to programming languages, but less investigated for knowledge representation purposes. The combination of a constructive language with a modal extension of contexts appears crucial to explore the attractive idea of a type-theoretical calculus of provability from refutable assumptions for non-monotonic reasoning. This paper introduces such a language: the modal operators are meant to internalize two different modes of correctness, respectively with necessity as the standard notion of constructive verification and possibility (...)
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  44. Guy Politzer (2007). Reasoning with Conditionals. Topoi 26 (1):79-95.score: 96.0
    This paper reviews the psychological investigation of reasoning with conditionals, putting an emphasis on recent work. In the first part, a few methodological remarks are presented. In the second part, the main theories of deductive reasoning (mental rules, mental models, and the probabilistic approach) are considered in turn; their content is summarised and the semantics they assume for if and the way they explain formal conditional reasoning are discussed, in particular in the light of experimental work on (...)
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  45. Hanti Lin (2013). Foundations of Everyday Practical Reasoning. Journal of Philosophical Logic 42 (6):831-862.score: 96.0
    “Since today is Saturday, the grocery store is open today and will be closed tomorrow; so let’s go today”. That is an example of everyday practical reasoningreasoning directly with the propositions that one believes but may not be fully certain of. Everyday practical reasoning is one of our most familiar kinds of decisions but, unfortunately, some foundational questions about it are largely ignored in the standard decision theory: (Q1) What are the decision rules in everyday practical (...) that connect qualitative belief and desire to preference over acts? (Q2) What sort of logic should govern qualitative beliefs in everyday practical reasoning, and to what extent is that logic necessary for the purposes of qualitative decisions? (Q3) What kinds of qualitative decisions are always representable as results of everyday practical reasoning? (Q4) Under what circumstances do the results of everyday practical reasoning agree with the Bayesian ideal of expected utility maximization? This paper proposes a rigorous decision theory for answering all of those questions, which is developed in parallel to Savage’s (1954) foundation of expected utility maximization. In light of a new representation result, everyday practical reasoning provides a sound and complete method for a very wide class of qualitative decisions; and, to that end, qualitative beliefs must be allowed to be closed under classical logic plus a well-known nonmonotonic logic—the so-called system ℙ. (shrink)
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  46. Douglas Walton, Christopher W. Tindale & Thomas F. Gordon (2014). Applying Recent Argumentation Methods to Some Ancient Examples of Plausible Reasoning. Argumentation 28 (1):85-119.score: 96.0
    Plausible (eikotic) reasoning known from ancient Greek (late Academic) skeptical philosophy is shown to be a clear notion that can be analyzed by argumentation methods, and that is important for argumentation studies. It is shown how there is a continuous thread running from the Sophists to the skeptical philosopher Carneades, through remarks of Locke and Bentham on the subject, to recent research in artificial intelligence. Eleven characteristics of plausible reasoning are specified by analyzing key examples of it recognized (...)
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  47. Steven O. Kimbrough & Hua Hua (1991). On Nonmonotonic Reasoning with the Method of Sweeping Presumptions. Minds and Machines 1 (4):393-416.score: 96.0
    Reasoning almost always occurs in the face of incomplete information. Such reasoning is nonmonotonic in the sense that conclusions drawn may later be withdrawn when additional information is obtained. There is an active literature on the problem of modeling such nonmonotonic reasoning, yet no category of method-let alone a single method-has been broadly accepted as the right approach. This paper introduces a new method, called sweeping presumptions, for modeling nonmonotonic (...)
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  48. Frank Zenker (2006). Monotonicity and Reasoning with Exceptions. Argumentation 20 (2):227-236.score: 96.0
    A proposal by Ferguson [2003, Argumentation 17, 335–346] for a fully monotonic argument form allowing for the expression of defeasible generalizations is critically examined and rejected as a general solution. It is argued that (i) his proposal reaches less than the default-logician’s solution allows, e.g., the monotonously derived conclusion is one-sided and itself not defeasible. (ii) when applied to a suitable example, his proposal derives the wrong conclusion. Unsuccessful remedies are discussed.
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  49. Gilbert Plumer & Kenneth Olson (2007). Reasoning From Conflicting Sources. In Hans V. Hansen, Christopher W. Tindale, J. Anthony Blair, Ralph H. Johnson & David M. Godden (eds.), Dissensus and the Search for Common Ground. Proceedings 2007 [CD-ROM]. Ontario Society for the Study of Argumentation.score: 92.0
    One might ask of two or more texts—what can be inferred from them, taken together? If the texts happen to contradict each other in some respect, then the unadorned answer of standard logic is EVERYTHING. But it seems to be a given that we often successfully reason with inconsistent information from multiple sources. The purpose of this paper is to attempt to develop an adequate approach to accounting for this given.
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  50. Jan-R. Sieckmann (2003). Why Non-Monotonic Logic is Inadequate to Represent Balancing Arguments. Artificial Intelligence and Law 11 (2-3):211-219.score: 90.0
    This paper analyses the logical structure of the balancing of conflicting normative arguments, and asks whether non-monotonic logic is adequate to represent this type of legal or practical reasoning. Norm conflicts are often regarded as a field of application for non-monotonic logics. This paper argues, however, that the balancing of normative arguments consists of an act of judgement, not a logical inference, and that models of deductive as well as of defeasible reasoning do not give an adequate (...)
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