
Disjunctive Answer Set Solvers via Templates
Answer set programming is a declarative programming paradigm oriented to...
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Abstract Modular Systems and Solvers
Integrating diverse formalisms into modular knowledge representation sys...
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Omissionbased Abstraction for Answer Set Programs
Abstraction is a wellknown approach to simplify a complex problem by ov...
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Translating AnswerSet Programs into BitVector Logic
Answer set programming (ASP) is a paradigm for declarative problem solvi...
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Transition Systems for Model Generators  A Unifying Approach
A fundamental task for propositional logic is to compute models of propo...
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Optimal Scheduling for Exposed Datapath Architectures with Buffered Processing Units by ASP
Conventional processor architectures are restricted in exploiting instru...
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Iterative Learning of Answer Set Programs from Context Dependent Examples
In recent years, several frameworks and systems have been proposed that ...
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Abstract Answer Set Solvers with Learning
Nieuwenhuis, Oliveras, and Tinelli (2006) showed how to describe enhancements of the DavisPutnamLogemannLoveland algorithm using transition systems, instead of pseudocode. We design a similar framework for several algorithms that generate answer sets for logic programs: Smodels, Smodelscc, AspSat with Learning (Cmodels), and a newly designed and implemented algorithm Sup. This approach to describing answer set solvers makes it easier to prove their correctness, to compare them, and to design new systems.
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