Probabilistic logic programming
Probabilistic logic programming is a programming paradigm that combines logic programming with probabilities. Most approaches to probabilistic logic programming are based on the distribution semantics,[1] with the main exception of Stochastic Logic Programs.
Distribution semantics
The distribution semantics gives meaning to a probabilistic logic program as a probability distribution over possible worlds. The distribution semantics[2][3] gives meaning to probabilistic logic programs that consist of an ordinary logic program and a set of probabilistic facts, which are logical facts annotated with a probability.
Languages
Inference
Learning
Applications
See also
- ^ Riguzzi, Fabrizio; Swift, Theresa (2018-09-01), "A survey of probabilistic logic programming", Declarative Logic Programming: Theory, Systems, and Applications, ACM, pp. 185–228, retrieved 2023-10-25
- ^ Poole, David (1993). "Probabilistic Horn abduction and Bayesian networks". Artificial Intelligence. 64 (1): 81–129. doi:10.1016/0004-3702(93)90061-f. ISSN 0004-3702.
- ^ "A Statistical Learning Method for Logic Programs with Distribution Semantics", Proceedings of the 12th International Conference on Logic Programming, The MIT Press, 1995, retrieved 2023-10-25