Probabilistic Logic Programming and Bayesian Networks Export

@inproceedings{citeulike:5345253, abstract = {We present a probabilistic logic programming framework that allows the representation of conditional probabilities. While conditional probabilities are the most commonly used method for representing uncertainty in probabilistic expert systems, they have been largely neglected by work in quantitative logic programming. We define a fixpoint theory, declarative semantics, and proof procedure for the new class of probabilistic logic programs. Compared to other approaches to quantitative logic programming, we provide a true probabilistic framework with potential applications in probabilistic expert systems and decision support systems. We also discuss the relationship between such programs and Bayesian networks, thus moving toward a unification of two major approaches to automated reasoning. To appear in Proceedings of the 1995 Asian Computing Science Conference, Pathumthani, Thailand, December 1995. This work was partially supported by NSF grant IRI-9509165. 1 Introduction Reasoning u...}, author = {Ngo, Liem and Haddawy, Peter}, booktitle = {In Asian Computing Science Conference}, citeulike-article-id = {5345253}, citeulike-linkout-0 = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.55.4357}, keywords = {bayesnet, logic, probabilistic, programming}, pages = {286--300}, posted-at = {2009-08-04 03:27:59}, priority = {2}, title = {Probabilistic Logic Programming and Bayesian Networks}, url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.55.4357}, volume = {1023}, year = {1995} }

TY - CONF ID - citeulike:5345253 L3 - citeulike-article-id:5345253 N2 - We present a probabilistic logic programming framework that allows the representation of conditional probabilities. While conditional probabilities are the most commonly used method for representing uncertainty in probabilistic expert systems, they have been largely neglected by work in quantitative logic programming. We define a fixpoint theory, declarative semantics, and proof procedure for the new class of probabilistic logic programs. Compared to other approaches to quantitative logic programming, we provide a true probabilistic framework with potential applications in probabilistic expert systems and decision support systems. We also discuss the relationship between such programs and Bayesian networks, thus moving toward a unification of two major approaches to automated reasoning. To appear in Proceedings of the 1995 Asian Computing Science Conference, Pathumthani, Thailand, December 1995. This work was partially supported by NSF grant IRI-9509165. 1 Introduction Reasoning u... EP - 300 TI - Probabilistic Logic Programming and Bayesian Networks VL - 1023 T2 - In Asian Computing Science Conference SP - 286 KW - bayesnet KW - logic KW - probabilistic KW - programming AU - Ngo, Liem AU - Haddawy, Peter PY - 1995/// UR - http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.55.4357 ER -