TY - GEN

T1 - The most probable explanation for probabilistic logic programs with annotated disjunctions

AU - Shterionov, Dimitar

AU - Renkens, Joris

AU - Vlasselaer, Jonas

AU - Kimmig, Angelika

AU - Meert, Wannes

AU - Janssens, Gerda

PY - 2015

Y1 - 2015

N2 - Probabilistic logic languages, such as ProbLog and CP-logic, are probabilistic generalizations of logic programming that allow one to model probability distributions over complex, structured domains. Their key probabilistic constructs are probabilistic facts and annotated disjunctions to represent binary and mutli-valued random variables, respectively. ProbLog allows the use of annotated disjunctions by translating them into probabilistic facts and rules. This encoding is tailored towards the task of computing the marginal probability of a query given evidence (MARG), but is not correct for the task of finding the most probable explanation (MPE) with important applications e.g., diagnostics and scheduling. In this work, we propose a new encoding of annotated disjunctions which allows correct MARG and MPE. We explore from both theoretical and experimental perspective the trade-off between the encoding suitable only for MARG inference and the newly proposed (general) approach.

AB - Probabilistic logic languages, such as ProbLog and CP-logic, are probabilistic generalizations of logic programming that allow one to model probability distributions over complex, structured domains. Their key probabilistic constructs are probabilistic facts and annotated disjunctions to represent binary and mutli-valued random variables, respectively. ProbLog allows the use of annotated disjunctions by translating them into probabilistic facts and rules. This encoding is tailored towards the task of computing the marginal probability of a query given evidence (MARG), but is not correct for the task of finding the most probable explanation (MPE) with important applications e.g., diagnostics and scheduling. In this work, we propose a new encoding of annotated disjunctions which allows correct MARG and MPE. We explore from both theoretical and experimental perspective the trade-off between the encoding suitable only for MARG inference and the newly proposed (general) approach.

KW - Logic programs with annotated disjunctions

KW - Most Probable Explanation

KW - Probabilistic logic programming

KW - ProbLog

KW - Statistical relational learning

UR - http://www.scopus.com/inward/record.url?scp=84954088095&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-23708-4_10

DO - 10.1007/978-3-319-23708-4_10

M3 - Conference contribution

AN - SCOPUS:84954088095

SN - 9783319237077

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 139

EP - 153

BT - Inductive Logic Programming - 24th International Conference, ILP 2014, Revised Selected Papers

A2 - Davis, Jesse

A2 - Ramon, Jan

PB - Springer Verlag

T2 - 24th International Conference on Inductive Logic Programming, ILP 2014

Y2 - 14 September 2014 through 16 September 2014

ER -