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  1. ; (, Logic Programming and Nonmonotonic Reasoning)
    Conditional literals are an expressive Answer Set Programming language construct supported by the solver clingo. Their semantics are currently defined by a translation to infinitary propositional logic, however, we develop an alternative characterization with the SM operator which does not rely on grounding. This allows us to reason about the behavior of a broad class of clingo programs/encodings containing conditional literals, without referring to a particular input/instance of an encoding. We formalize the intuition that conditional literals behave as nested implications, and prove the equivalence of our semantics to those implemented by clingo. 
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  2. ; ; (, Arguing Correctness of ASP Programs with Aggregates)
    This paper studies the problem of arguing program correctness for logic programs with aggregates in the context of Answer Set Programming. Cabalar, Fandinno, and Lierler (2020) championed a modular methodology for arguing program correctness. We show how a recently proposed many-sorted semantics for logic programs with aggregates allows us to apply their methodology to this type of program. This is illustrated using well-known encodings for the Graph Coloring and Traveling Salesman problems. In particular, we showcase how this modular approach allows us to reuse the proof of correctness of a Hamiltonian Cycle encoding studied in a previous publication when considering the Traveling Salesman program. 
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  3. ; ; (, Logic Programming and Nonmonotonic Reasoning)
    Answer set programming (ASP) has long been used for modeling and solving hard search problems. Experience shows that the performance of ASP tools on different ASP encodings of the same problem may vary greatly from instance to instance and it is rarely the case that one encoding outperforms all others. We describe a system and its implementation that given a set of encodings and a training set of instances, builds performance models for the encodings, predicts the execution time of these encodings on new instances, and uses these predictions to select an encoding for solving. 
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  4. (, Proceedings of the 21st International Symposium on Practical Aspects of Declarative Languages (PADL))
    Accepted Manuscript Full Text Available
  5. ; (, 21st International Symposium on Practical Aspects of Declarative Languages (PADL))
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