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  1. Steps toward formalizing context.Varol Akman & Mehmet Surav - 1996 - AI Magazine 17 (3):55-72.
    The importance of contextual reasoning is emphasized by various researchers in AI. (A partial list includes John McCarthy and his group, R. V. Guha, Yoav Shoham, Giuseppe Attardi and Maria Simi, and Fausto Giunchiglia and his group.) Here, we survey the problem of formalizing context and explore what is needed for an acceptable account of this abstract notion.
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  • (1 other version)Linear reasoning. A new form of the herbrand-Gentzen theorem.William Craig - 1957 - Journal of Symbolic Logic 22 (3):250-268.
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  • Generating hard satisfiability problems.Bart Selman, David G. Mitchell & Hector J. Levesque - 1996 - Artificial Intelligence 81 (1-2):17-29.
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  • Interpolants, cut elimination and flow graphs for the propositional calculus.Alessandra Carbone - 1997 - Annals of Pure and Applied Logic 83 (3):249-299.
    We analyse the structure of propositional proofs in the sequent calculus focusing on the well-known procedures of Interpolation and Cut Elimination. We are motivated in part by the desire to understand why a tautology might be ‘hard to prove’. Given a proof we associate to it a logical graph tracing the flow of formulas in it . We show some general facts about logical graphs such as acyclicity of cut-free proofs and acyclicity of contraction-free proofs , and we give a (...)
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  • Interpolation theorems, lower Bounds for proof systems, and independence results for bounded arithmetic.Jan Krajíček - 1997 - Journal of Symbolic Logic 62 (2):457-486.
    A proof of the (propositional) Craig interpolation theorem for cut-free sequent calculus yields that a sequent with a cut-free proof (or with a proof with cut-formulas of restricted form; in particular, with only analytic cuts) with k inferences has an interpolant whose circuit-size is at most k. We give a new proof of the interpolation theorem based on a communication complexity approach which allows a similar estimate for a larger class of proofs. We derive from it several corollaries: (1) Feasible (...)
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  • Three uses of the herbrand-Gentzen theorem in relating model theory and proof theory.William Craig - 1957 - Journal of Symbolic Logic 22 (3):269-285.
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  • Linear resolution for consequence finding.Katsumi Inoue - 1992 - Artificial Intelligence 56 (2-3):301-353.
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  • A note on linear resolution strategies in consequence-finding.Eliana Minicozzi & Raymond Reiter - 1972 - Artificial Intelligence 3 (C):175-180.
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  • Enhancement schemes for constraint processing: Backjumping, learning, and cutset decomposition.Rina Dechter - 1990 - Artificial Intelligence 41 (3):273-312.
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  • Tree clustering for constraint networks.Rina Dechter & Judea Pearl - 1989 - Artificial Intelligence 38 (3):353-366.
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  • Formalizing context (expanded notes).John McCarthy & Sasa Buvac - 1998 - CSLI Lecture Notes 81:13-50.
    These notes discuss formalizing contexts as first class objects. The basic relationships are: ist(c,p) meaning that the proposition p is true in the context c, and value(c,p) designating the value of the term e in the context c Besides these, there are lifting formulas that relate the propositions and terms in subcontexts to possibly more general propositions and terms in the outer context. Subcontextx are often specialised with regard to time, place and terminology. Introducing contexts as formal objects will permit (...)
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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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  • BREAKUP: a preprocessing algorithm for satisfiability testing of CNF formulas.Robert Cowen & Katherine Wyatt - 1993 - Notre Dame Journal of Formal Logic 34 (4):602-606.
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  • Topological parameters for time-space tradeoff.Rina Dechter & Yousri El Fattah - 2001 - Artificial Intelligence 125 (1-2):93-118.
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