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  1. A consistency-based framework for merging knowledge bases.James P. Delgrande & Torsten Schaub - 2007 - Journal of Applied Logic 5 (3):459-477.
  • Temporal reasoning about fuzzy intervals.Steven Schockaert & Martine De Cock - 2008 - Artificial Intelligence 172 (8-9):1158-1193.
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  • Spatial reasoning in a fuzzy region connection calculus.Steven Schockaert, Martine De Cock & Etienne E. Kerre - 2009 - Artificial Intelligence 173 (2):258-298.
  • 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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  • Toward a mechanistic psychology of dialogue.Martin J. Pickering & Simon Garrod - 2004 - Behavioral and Brain Sciences 27 (2):169-190.
    Traditional mechanistic accounts of language processing derive almost entirely from the study of monologue. Yet, the most natural and basic form of language use is dialogue. As a result, these accounts may only offer limited theories of the mechanisms that underlie language processing in general. We propose a mechanistic account of dialogue, the interactive alignment account, and use it to derive a number of predictions about basic language processes. The account assumes that, in dialogue, the linguistic representations employed by the (...)
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  • Integration of weighted knowledge bases.Jinxin Lin - 1996 - Artificial Intelligence 83 (2):363-378.
  • Reasoning under inconsistency: A forgetting-based approach.Jérôme Lang & Pierre Marquis - 2010 - Artificial Intelligence 174 (12-13):799-823.
  • Hedges: A study in meaning criteria and the logic of fuzzy concepts. [REVIEW]George Lakoff - 1973 - Journal of Philosophical Logic 2 (4):458 - 508.
  • Nonmonotonic reasoning, preferential models and cumulative logics.Sarit Kraus, Daniel Lehmann & Menachem Magidor - 1990 - Artificial Intelligence 44 (1-2):167-207.
  • DA2 merging operators.S. Konieczny, J. Lang & P. Marquis - 2004 - Artificial Intelligence 157 (1-2):49-79.
  • Qualitative probabilities for default reasoning, belief revision, and causal modeling.Moisés Goldszmidt & Judea Pearl - 1996 - Artificial Intelligence 84 (1-2):57-112.
    This paper presents a formalism that combines useful properties of both logic and probabilities. Like logic, the formalism admits qualitative sentences and provides symbolic machinery for deriving deductively closed beliefs and, like probability, it permits us to express if-then rules with different levels of firmness and to retract beliefs in response to changing observations. Rules are interpreted as order-of-magnitude approximations of conditional probabilities which impose constraints over the rankings of worlds. Inferences are supported by a unique priority ordering on rules (...)
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  • Temporal reasoning based on semi-intervals.Christian Freksa - 1992 - Artificial Intelligence 54 (1-2):199-227.
  • Epistemic entrenchment and possibilistic logic.Didier Dubois & Henri Prade - 1991 - Artificial Intelligence 50 (2):223-239.
  • Upper and Lower Probabilities induced by a Multi- valued Mapping.Arthur Dempster - 1967 - Annals of Mathematical Statistics 38:325-339.
     
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