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Most approaches to probabilistic logic programming are based on the ''distribution semantics,'' which splits a program into a set of probabilistic facts and a logic program. It defines a probability distribution on interpretations of the [[Herbrand structure|Herbrand universe]] of the program.
== Languages ==
Most approaches to probabilistic logic programming are based on the ''distribution semantics,''<ref name=":3">{{Citation |last=Riguzzi |first=Fabrizio |title=A survey of probabilistic logic programming |date=2018-09-01 |work=Declarative Logic Programming: Theory, Systems, and Applications |pages=185–228 |url=http://dx.doi.org/10.1145/3191315.3191319 |access-date=2023-10-25 |publisher=ACM |last2=Swift |first2=Theresa}}</ref> which underlies many languages such as Probabilistic Horn Abduction, PRISM, Independent Choice Logic , probabilistic [[Datalog]], Logic Programs with Annotated Disjunctions, [[ProbLog]], P-log, and CP-logic. While the number of languages is large, all share a common approach so that there are transformations with [[Time complexity|linear complexity]] that can translate one language into another.<ref name=":0">{{Cite journal |last=Riguzzi |first=Fabrizio |last2=Bellodi |first2=Elena |last3=Zese |first3=Riccardo |date=2014 |title=A History of Probabilistic Inductive Logic Programming |url=https://www.frontiersin.org/articles/10.3389/frobt.2014.00006 |journal=Frontiers in Robotics and AI |volume=1 |doi=10.3389/frobt.2014.00006/full |issn=2296-9144}}</ref>
== Semantics ==
Under the distribution semantics, a probabilistic logic program is interpreted as a set of independent probabilistic facts ([[Ground expression|ground]] [[Atomic formula|atomic formulas]] annotated with a probability) and a [[logic program]] which can use the probabilistic facts in the bodies of its clauses. The probability of any assignment of truth values to the groundings of the formulas associated with probabilistic facts is given by the product of their probabilities; this is equivalent to assuming the choices of probabilistic facts to be [[independent random variables]].<ref name=":3" /><ref>{{Cite journal |last=De Raedt |first=Luc |last2=Kimmig |first2=Angelika |date=2015-07-01 |title=Probabilistic (logic) programming concepts |url=https://doi.org/10.1007/s10994-015-5494-z |journal=Machine Learning |language=en |volume=100 |issue=1 |pages=5–47 |doi=10.1007/s10994-015-5494-z |issn=1573-0565}}</ref>
=== Stratified programs ===
If for any choice of truth values for the probabilistic facts, the resulting logic program is [[Stratified logic program|stratified]], it has a unique minimal [[Herbrand model]] which can be seen as the unique interpretation associated with that choice of truth values.<ref name=":3" />
Important subclasses of stratified programs are positive programs, which do not use negation, but may be recursive, and acyclic programs, which may use negation but have no recursive dependencies.<ref name=":3" />
=== Answer set programs ===
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