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  1. Iterated belief revision, revised.Yi Jin & Michael Thielscher - 2007 - Artificial Intelligence 171 (1):1-18.
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  • What's new isn't always best.Sven Ove Hansson - 1997 - Theoria 63 (1-2):1-13.
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  • Minimally inconsistent LP.Graham Priest - 1991 - Studia Logica 50 (2):321 - 331.
    The paper explains how a paraconsistent logician can appropriate all classical reasoning. This is to take consistency as a default assumption, and hence to work within those models of the theory at hand which are minimally inconsistent. The paper spells out the formal application of this strategy to one paraconsistent logic, first-order LP. (See, Ch. 5 of: G. Priest, In Contradiction, Nijhoff, 1987.) The result is a strong non-monotonic paraconsistent logic agreeing with classical logic in consistent situations. It is shown (...)
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  • (1 other version)A logical measure function.John G. Kemeny - 1953 - Journal of Symbolic Logic 18 (4):289-308.
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  • (1 other version)On the logic of theory change: Partial meet contraction and revision functions.Carlos E. Alchourrón, Peter Gärdenfors & David Makinson - 1985 - Journal of Symbolic Logic 50 (2):510-530.
    This paper extends earlier work by its authors on formal aspects of the processes of contracting a theory to eliminate a proposition and revising a theory to introduce a proposition. In the course of the earlier work, Gardenfors developed general postulates of a more or less equational nature for such processes, whilst Alchourron and Makinson studied the particular case of contraction functions that are maximal, in the sense of yielding a maximal subset of the theory (or alternatively, of one of (...)
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  • A theory of diagnosis from first principles.Raymond Reiter - 1987 - Artificial Intelligence 32 (1):57-95.
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  • On the logic of iterated belief revision.Adnan Darwiche & Judea Pearl - 1997 - Artificial Intelligence 89 (1-2):1-29.
    We show in this paper that the AGM postulates are too weak to ensure the rational preservation of conditional beliefs during belief revision, thus permitting improper responses to sequences of observations. We remedy this weakness by proposing four additional postulates, which are sound relative to a qualitative version of probabilistic conditioning. Contrary to the AGM framework, the proposed postulates characterize belief revision as a process which may depend on elements of an epistemic state that are not necessarily captured by a (...)
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  • Belief base merging as a game.Sébastien Konieczny - 2004 - Journal of Applied Non-Classical Logics 14 (3):275-294.
    We propose in this paper a new family of belief merging operators, that is based on a game between sources : until a coherent set of sources is reached, at each round a contest is organized to find out the weakest sources, then those sources has to concede. This idea leads to numerous new interesting operators and opens new perspectives for belief merging. Some existing operators are also recovered as particular cases. Those operators can be seen as a special case (...)
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  • On Inferences from Inconsistent Premises.Nicholas Rescher & Ruth Manor - 1970 - Theory and Decision 1 (2):179-217, 1970-1971.
    The main object of this paper is to provide the logical machinery needed for a viable basis for talking of the ‘consequences’, the ‘content’, or of ‘equivalences’ between inconsistent sets of premisses.With reference to its maximal consistent subsets (m.c.s.), two kinds of ‘consequences’ of a propositional set S are defined. A proposition P is a weak consequence (W-consequence) of S if it is a logical consequence of at least one m.c.s. of S, and P is an inevitable consequence (I-consequence) of (...)
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  • Two information measures for inconsistent sets.Kevin M. Knight - 2003 - Journal of Logic, Language and Information 12 (2):227-248.
    I present two measures of information for both consistentand inconsistent sets of sentences in a finite language ofpropositional logic. The measures of information are based onmeasures of inconsistency developed in Knight (2002).Relative information measures are then provided corresponding to thetwo information measures.
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  • Measuring inconsistency.Kevin Knight - 2002 - Journal of Philosophical Logic 31 (1):77-98.
    I provide a method of measuring the inconsistency of a set of sentences from 1-consistency, corresponding to complete consistency, to 0-consistency, corresponding to the explicit presence of a contradiction. Using this notion to analyze the lottery paradox, one can see that the set of sentences capturing the paradox has a high degree of consistency (assuming, of course, a sufficiently large lottery). The measure of consistency, however, is not limited to paradoxes. I also provide results for general sets of sentences.
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  • Classifications for inconsistent theories.John Grant - 1978 - Notre Dame Journal of Formal Logic 19 (3):435-444.
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  • Some syntactic approaches to the handling of inconsistent knowledge bases: A comparative study part 1: The flat case.Salem Benferhat, Didier Dubois & Henri Prade - 1997 - Studia Logica 58 (1):17-45.
    This paper presents and discusses several methods for reasoning from inconsistent knowledge bases. A so-called argued consequence relation, taking into account the existence of consistent arguments in favour of a conclusion and the absence of consistent arguments in favour of its contrary, is particularly investigated. Flat knowledge bases, i.e., without any priority between their elements, are studied under different inconsistency-tolerant consequence relations, namely the so-called argumentative, free, universal, existential, cardinality-based, and paraconsistent consequence relations. The syntax-sensitivity of these consequence relations is (...)
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  • (1 other version)What does a conditional knowledge base entail?Daniel Lehmann & Menachem Magidor - 1992 - Artificial Intelligence 55 (1):1-60.
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  • On the complexity of propositional knowledge base revision, updates, and counterfactuals.Thomas Eiter & Georg Gottlob - 1992 - Artificial Intelligence 57 (2-3):227-270.
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  • DA2 merging operators.S. Konieczny, J. Lang & P. Marquis - 2004 - Artificial Intelligence 157 (1-2):49-79.
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