In Probabilistic Logic Programming, a large number of languages have been independently proposed. Many of these however follow a common approach, the distribution semantics (Sato 1995). Since PLP systems generally must solve a large number of inference problems in order to perform learning, they rely critically on the support of efficient inference systems. The talk will provide an overview of the most recent and scalable techniques for exact and approximate reasoning on PLP programs under the distribution semantics, in the presence of discrete or continuous random variables.
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