Probabilistic logic programming: Difference between revisions

Revision as of 14:12, 3 February 2024

Revision as of 14:13, 3 February 2024

Line 4:

Most approaches to probabilistic logic programming are based on the ''distribution semantics''.<ref>{{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>

Under the distribution semantics, a probabilistic logic program defines a probability distribution over [[Interpretation (logic)|interpretations]] of its predicates on its [[Herbrand Universe|Herbrand universe]]. The probability of a [[Ground expression|ground]] query ''Q'' is then obtained from the [[Joint probability distribution|joint distribution]] of the query and the worlds: it is the sum of the probability of the worlds where the query is true.<ref name=":0" /><ref>{{Cite journal |last=Poole |first=David |date=1993 |title=Probabilistic Horn abduction and Bayesian networks |url=http://dx.doi.org/10.1016/0004-3702(93)90061-f |journal=Artificial Intelligence |volume=64 |issue=1 |pages=81–129 |doi=10.1016/0004-3702(93)90061-f |issn=0004-3702}}</ref><ref>{{Citation |last=Sato |first=Taisuke |title=A Statistical Learning Method for Logic Programs with Distribution Semantics |date=1995 |work=Proceedings of the 12th International Conference on Logic Programming |url=http://dx.doi.org/10.7551/mitpress/4298.003.0069 |access-date=2023-10-25 |publisher=The MIT Press}}</ref>

== Languages ==