Lazy explanations for constraint propagators
PADL'10 Proceedings of the 12th international conference on Practical Aspects of Declarative Languages
τε2asp: implementing τε via answer set programming
PRICAI'12 Proceedings of the 12th Pacific Rim international conference on Trends in Artificial Intelligence
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During the last decade SAT techniques have become very successful for practice, with important impact in applications such as electronic design automation. DPLL-based clause-learning SAT solvers work surprisingly well on real-world problems from many sources, using a single, fully automatic, push-button strategy. Hence, modeling and using SAT is essentially a declarative task. On the negative side, propositional logic is a very low level language and hence modeling and encoding tools are required. Also, the answer can only be "unsatisfiable" (possibly with a proof) or a model: optimization aspects are not as well studied.