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  1. An incremental algorithm for generating all minimal models.Rachel Ben-Eliyahu – Zohary - 2005 - Artificial Intelligence 169 (1):1-22.
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  • Extending and implementing the stable model semantics.Patrik Simons, Ilkka Niemelä & Timo Soininen - 2002 - Artificial Intelligence 138 (1-2):181-234.
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  • LARS: A Logic-based framework for Analytic Reasoning over Streams.Harald Beck, Minh Dao-Tran & Thomas Eiter - 2018 - Artificial Intelligence 261 (C):16-70.
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  • A general framework for reason maintenance.Drew McDermott - 1991 - Artificial Intelligence 50 (3):289-329.
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  • Is logicist cognitive science possible?Alan Garnham - 1993 - Mind and Language 8 (1):49-71.
    This paper argues against Oaksford and Chater's claim that logicist cognitive science is not possible. It suggests that there arguments against logicist cognitive science are too closely tied to the account of Pylyshyn and of Fodor, and that the correct way of thinking about logicist cognitive science is in a mental models framework.
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  • Modeling the suppression task under weak completion and well-founded semantics.Emmanuelle-Anna Dietz, Steffen Hölldobler & Christoph Wernhard - 2014 - Journal of Applied Non-Classical Logics 24 (1-2):61-85.
    Formal approaches that aim at representing human reasoning should be evaluated based on how humans actually reason. One way of doing so is to investigate whether psychological findings of human reasoning patterns are represented in the theoretical model. The computational logic approach discussed here is the so-called weak completion semantics which is based on the three-valued ᴌukasiewicz logic. We explain how this approach adequately models Byrne’s suppression task, a psychological study where the experimental results show that participants’ conclusions systematically deviate (...)
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  • On the impact of stratification on the complexity of nonmonotonic reasoning.Ilkka Niemelä & Jussi Rintanen - 1994 - Journal of Applied Non-Classical Logics 4 (2):141-179.
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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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  • (1 other version)Logic programming and knowledge representation—The A-Prolog perspective.Michael Gelfond & Nicola Leone - 2002 - Artificial Intelligence 138 (1-2):3-38.
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  • (1 other version)Non monotonic reasoning and belief revision: syntactic, semantic, foundational and coherence approaches.Alvaro del Val - 1997 - Journal of Applied Non-Classical Logics 7 (1-2):213-240.
    ABSTRACT The major approaches to belief revision and non monotonic reasoning proposed in the literature differ along a number of dimensions, including whether they are “syntax- based” or “semantic-based”, “foundational” or “coherentist”, “consistence-restoring” or “inconsistency-tolerant”. Our contribution towards clarifying the connections between these various approaches is threefold: •We show that the two main approaches to belief revision, the foundations and coherence theories, are mathematically equivalent, thus answering a question left open in [Gar90, Doy92], The distinction between syntax-based approaches to revision (...)
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  • (1 other version)Non monotonic reasoning and belief revision: syntactic, semantic, foundational and coherence approaches.Alvaro del Val - 1997 - Journal of Applied Non-Classical Logics 7 (1-2):213-240.
    ABSTRACT The major approaches to belief revision and non monotonic reasoning proposed in the literature differ along a number of dimensions, including whether they are ?syntax- based? or ?semantic-based?, ?foundational? or ?coherentist?, ?consistence-restoring? or ?inconsistency-tolerant?. Our contribution towards clarifying the connections between these various approaches is threefold: ?We show that the two main approaches to belief revision, the foundations and coherence theories, are mathematically equivalent, thus answering a question left open in [Gar90, Doy92], The distinction between syntax-based approaches to revision (...)
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