ASSAT: computing answer sets of a logic program by SAT solvers

By Fangzhen Lin, Yuting Zhao. Artificial Intelligence, 2002

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Conflict-Driven Answer Set Solving (2007, background): “Note that a loop formula can be constructed for any set of atoms, even if this set is not a loop in the sense of [Lin and Zhao, 2004].”
The DLV system for knowledge representation and reasoning (2002, methodology): “In particular, we compare the performance of DLV with two state-of-the-art ASP systems, namely, Smodels [Simons et al. 2002; Niemel¨ a et al. 2000; Niemel¨ a and Simons 1997] and ASSAT [Lin and Zhao 2002].”
Weighted Rules under the Stable Model Semantics (2016, unknown): “The theorem can be generalized to non-tight programs by considering loop formulas (Lin and Zhao 2004), which we skip here for brevity.”
Conflict-driven answer set solving: From theory to practice (2012, methodology): “Our approach is guided by the idea of Lin and Zhao [90] and decomposes ASP solving into " local " inferences obtainable from the Clark completion [21] of a program and those obtainable from loop formulas [90].”
Answer Set Solving in Practice (2012, unknown): “[56] F.”
Potassco: The Potsdam Answer Set Solving Collection (2011, unknown): “2 The success story of ASP has its roots in the early availability of ASP solvers, beginning with the smodels system [95], followed by dlv [75], SAT-based ASP solvers, like assat [80] and cmodels [61], and the conflict-driven learning ASP solver clasp [48], demonstrating the performance and versatil”