Graph-Based Algorithms for Boolean Function Manipulation
IEEE Transactions on Computers
The well-founded semantics for general logic programs
Journal of the ACM (JACM)
On the Construction of Multiple-Valued Decision Diagrams
ISMVL '02 Proceedings of the 32nd International Symposium on Multiple-Valued Logic
ProbLog: a probabilistic prolog and its application in link discovery
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Representing causal information about a probabilistic process
JELIA'06 Proceedings of the 10th European conference on Logics in Artificial Intelligence
On the Efficient Execution of ProbLog Programs
ICLP '08 Proceedings of the 24th International Conference on Logic Programming
Inference with Logic Programs with Annotated Disjunctions under the Well Founded Semantics
ICLP '08 Proceedings of the 24th International Conference on Logic Programming
Logic–based decision support for strategic environmental assessment
Theory and Practice of Logic Programming
SLGAD Resolution for Inference on Logic Programs with Annotated Disjunctions
Fundamenta Informaticae - RCRA 2008 Experimental Evaluation of Algorithms for Solving Problems with Combinatorial Explosion
ILP'09 Proceedings of the 19th international conference on Inductive logic programming
On the implementation of the probabilistic logic programming language problog
Theory and Practice of Logic Programming
Approximate inference for logic programs with annotated disjunctions
ILP'10 Proceedings of the 20th international conference on Inductive logic programming
Theory and Practice of Logic Programming - Prolog Systems
MCINTYRE: A Monte Carlo System for Probabilistic Logic Programming
Fundamenta Informaticae - Special Issue on the Italian Conference on Computational Logic: CILC 2011
Expectation maximization over binary decision diagrams for probabilistic logic programs
Intelligent Data Analysis
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Logic Programs with Annotated Disjunctions and CP-logic are two different but related languages for expressing probabilistic information in logic programming. The paper presents a top down interpreter for computing the probability of a query from a program in one of these two languages. The algorithm is based on the one available for ProbLog. The performances of the algorithm are compared with those of a Bayesian reasoner and with those of the ProbLog interpreter. On programs that have a small grounding, the Bayesian reasoner is more scalable, but programs with a large grounding require the top down interpreter. The comparison with ProbLog shows that the added expressiveness effectively requires more computation resources.