Selected papers of international conference on Fifth generation computer systems 92
The independent choice logic for modelling multiple agents under uncertainty
Artificial Intelligence - Special issue on economic principles of multi-agent systems
Probabilistic Datalog: implementing logical information retrieval for advanced applications
Journal of the American Society for Information Science
Probabilistic Logic Programs and their Semantics
Proceedings of the First Russian Conference on Logic Programming
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
Modern Engineering Statistics
Compressing probabilistic Prolog programs
Machine Learning
ALLPAD: Approximate Learning of Logic Programs with Annotated Disjunctions
Inductive Logic Programming
A Top Down Interpreter for LPAD and CP-Logic
AI*IA '07 Proceedings of the 10th Congress of the Italian Association for Artificial Intelligence on AI*IA 2007: Artificial Intelligence and Human-Oriented Computing
Parameter Learning in Probabilistic Databases: A Least Squares Approach
ECML PKDD '08 Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I
Inference with Logic Programs with Annotated Disjunctions under the Well Founded Semantics
ICLP '08 Proceedings of the 24th International Conference on Logic Programming
Non-discriminating Arguments and Their Uses
ICLP '09 Proceedings of the 25th International Conference on Logic Programming
Parameter learning of logic programs for symbolic-statistical modeling
Journal of Artificial Intelligence Research
ProbLog: a probabilistic prolog and its application in link discovery
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Cp-logic: A language of causal probabilistic events and its relation to logic programming
Theory and Practice of Logic Programming
Probabilistic inductive logic programming: theory and applications
Probabilistic inductive logic programming: theory and applications
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
Learning the parameters of probabilistic logic programs from interpretations
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part I
Link discovery in graphs derived from biological databases
DILS'06 Proceedings of the Third international conference on Data Integration in the Life Sciences
Theory and Practice of Logic Programming - Prolog Systems
Learning the structure of probabilistic logic programs
ILP'11 Proceedings of the 21st international conference on Inductive Logic Programming
Expectation maximization over binary decision diagrams for probabilistic logic programs
Intelligent Data Analysis
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Probabilistic Logic Programming is receiving an increasing attention for its ability to model domains with complex and uncertain relations among entities. In this paper we concentrate on the problem of approximate inference in probabilistic logic programming languages based on the distribution semantics. A successful approximate approach is based on Monte Carlo sampling, that consists in verifying the truth of the query in a normal program sampled from the probabilistic program. The ProbLog system includes such an algorithm and so does the cplint suite. In this paper we propose an approach for Monte Carlo inference that is based on a program transformation that translates a probabilistic program into a normal program to which the query can be posed. The current sample is stored in the internal database of the Yap Prolog engine. The resulting system, called MCINTYRE for Monte Carlo INference wiTh Yap REcord, is evaluated on various problems: biological networks, artificial datasets and a hidden Markov model. MCINTYRE is compared with the Monte Carlo algorithms of ProbLog and cplint and with the exact inference of the PITA system. The results show that MCINTYRE is faster than the other Monte Carlo systems.