Abstract
Although negative conclusions are presented implicitly in Normal Hybrid Probabilistic Programs (NHPP) [26] through the closed world assumption, representing and reasoning with explicit negation is needed in NHPP to allow the ability to reason with incomplete knowledge. In this paper we extend the language of NHPP to explicitly encode classical negation in addition to non-monotonic negation. The semantics of the extended language is based on the answer set semantics of traditional logic programming [9]. We show that the proposed semantics is a natural extension to the answer set semantics of traditional logic programming [9]. In addition, the proposed semantics is reduced to stable probabilistic model semantics of NHPP [26]. The importance of that is computational methods developed for NHPP can be applied to the proposed language. Furthermore, we show that some commonsense probabilistic knowledge can be easily represented in the proposed language.
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Saad, E. (2006). Incomplete Knowledge in Hybrid Probabilistic Logic Programs. In: Fisher, M., van der Hoek, W., Konev, B., Lisitsa, A. (eds) Logics in Artificial Intelligence. JELIA 2006. Lecture Notes in Computer Science(), vol 4160. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11853886_33
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DOI: https://doi.org/10.1007/11853886_33
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