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  1. On the power of clause-learning SAT solvers as resolution engines.Knot Pipatsrisawat & Adnan Darwiche - 2011 - Artificial Intelligence 175 (2):512-525.
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  • Semantics and complexity of recursive aggregates in answer set programming.Wolfgang Faber, Gerald Pfeifer & Nicola Leone - 2011 - Artificial Intelligence 175 (1):278-298.
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  • ASSAT: computing answer sets of a logic program by SAT solvers.Fangzhen Lin & Yuting Zhao - 2004 - Artificial Intelligence 157 (1-2):115-137.
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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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  • 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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  • Answer set programming and plan generation.Vladimir Lifschitz - 2002 - Artificial Intelligence 138 (1-2):39-54.
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  • Some (in)translatability results for normal logic programs and propositional theories.Tomi Janhunen - 2006 - Journal of Applied Non-Classical Logics 16 (1-2):35-86.
    In this article, we compare the expressive powers of classes of normal logic programs that are obtained by constraining the number of positive subgoals in the bodies of rules. The comparison is based on the existence/nonexistence of polynomial, faithful, and modular translation functions between the classes. As a result, we obtain a strict ordering among the classes under consideration. Binary programs are shown to be as expressive as unconstrained programs but strictly more expressive than unary programs which, in turn, are (...)
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