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  1. A logic for default reasoning.Ray Reiter - 1980 - Artificial Intelligence 13 (1-2):81-137.
  • A typed resolution principle for deduction with conditional typing theory.Tie-Cheng Wang - 1995 - Artificial Intelligence 75 (2):161-194.
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  • The relative complexity of analytic tableaux and SL-resolution.André Vellino - 1993 - Studia Logica 52 (2):323 - 337.
    In this paper we describe an improvement of Smullyan's analytic tableau method for the propositional calculus-Improved Parent Clash Restricted (IPCR) tableau-and show that it is equivalent to SL-resolution in complexity.
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  • Compulsory reduction in linear derivation systems.Geoff Sutcliffe - 1991 - Artificial Intelligence 50 (1):131-132.
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  • Refutation graphs.Robert E. Shostak - 1976 - Artificial Intelligence 7 (1):51-64.
  • A simplified problem reduction format.David A. Plaisted - 1982 - Artificial Intelligence 18 (2):227-261.
  • The Q∗ algorithm—a search strategy for a deductive question-answering system.Jack Minker, Daniel H. Fishman & James R. McSkimin - 1973 - Artificial Intelligence 4 (3-4):225-243.
  • Reciprocal Influences Between Proof Theory and Logic Programming.Dale Miller - 2019 - Philosophy and Technology 34 (1):75-104.
    The topics of structural proof theory and logic programming have influenced each other for more than three decades. Proof theory has contributed the notion of sequent calculus, linear logic, and higher-order quantification. Logic programming has introduced new normal forms of proofs and forced the examination of logic-based approaches to the treatment of bindings. As a result, proof theory has responded by developing an approach to proof search based on focused proof systems in which introduction rules are organized into two alternating (...)
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  • Logic and the complexity of reasoning.Hector J. Levesque - 1988 - Journal of Philosophical Logic 17 (4):355 - 389.
  • Linear resolution with selection function.Robert Kowalski & Donald Kuehner - 1971 - Artificial Intelligence 2 (3-4):227-260.
  • The Complexity of Resolution Refinements.Joshua Buresh-Oppenheim & Toniann Pitassi - 2007 - Journal of Symbolic Logic 72 (4):1336 - 1352.
    Resolution is the most widely studied approach to propositional theorem proving. In developing efficient resolution-based algorithms, dozens of variants and refinements of resolution have been studied from both the empirical and analytic sides. The most prominent of these refinements are: DP (ordered). DLL (tree), semantic, negative, linear and regular resolution. In this paper, we characterize and study these six refinements of resolution. We give a nearly complete characterization of the relative complexities of all six refinements. While many of the important (...)
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  • Linear resolution for consequence finding.Katsumi Inoue - 1992 - Artificial Intelligence 56 (2-3):301-353.
  • Clause trees: a tool for understanding and implementing resolution in automated reasoning.J. D. Horton & Bruce Spencer - 1997 - Artificial Intelligence 92 (1-2):25-89.
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  • Semantical Analysis of the Logic of Bunched Implications.Alexander V. Gheorghiu & David J. Pym - 2023 - Studia Logica 111 (4):525-571.
    We give a novel approach to proving soundness and completeness for a logic (henceforth: the object-logic) that bypasses truth-in-a-model to work directly with validity. Instead of working with specific worlds in specific models, we reason with eigenworlds (i.e., generic representatives of worlds) in an arbitrary model. This reasoning is captured by a sequent calculus for a _meta_-logic (in this case, first-order classical logic) expressive enough to capture the semantics of the object-logic. Essentially, one has a calculus of validity for the (...)
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  • Reduction rules for resolution-based systems.Norbert Eisinger, Hans Jürgen Ohlbach & Axel Präcklein - 1991 - Artificial Intelligence 50 (2):141-181.
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