Abstract
Nonmonotonic causal logic became a basis for the semantics of several expressive action languages. Norman McCain and Paolo Ferraris showed how to embed propositional causal theories into logic programming, and this work paved the way to the use of answer set solvers for answering queries about actions described in causal logic. In this paper we generalize these embeddings to first-order causal logic—a system that has been used to simplify the semantics of variables in action descriptions.
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Lifschitz, V., Yang, F. (2010). Translating First-Order Causal Theories into Answer Set Programming. In: Janhunen, T., Niemelä, I. (eds) Logics in Artificial Intelligence. JELIA 2010. Lecture Notes in Computer Science(), vol 6341. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15675-5_22
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DOI: https://doi.org/10.1007/978-3-642-15675-5_22
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