The independent choice logic for modelling multiple agents under uncertainty
Artificial Intelligence - Special issue on economic principles of multi-agent systems
Using weighted MAX-SAT engines to solve MPE
Eighteenth national conference on Artificial intelligence
Machine Learning
Monte-Carlo algorithms for enumeration and reliability problems
SFCS '83 Proceedings of the 24th Annual Symposium on Foundations of Computer Science
PRISM: a language for symbolic-statistical modeling
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
ProbLog: a probabilistic prolog and its application in link discovery
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
IBAL: a probabilistic rational programming language
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
BLOG: probabilistic models with unknown objects
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
On the implementation of the probabilistic logic programming language problog
Theory and Practice of Logic Programming
A general MCMC method for Bayesian inference in logic-based probabilistic modeling
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
| Hi-index | 0.00 |
Probabilistic logic programming languages are powerful formalisms that can model complex problems where it is necessary to represent both structure and uncertainty. Using exact inference methods to compute conditional probabilities in these languages is often intractable so approximate inference techniques are necessary. This paper proposes a Markov Chain Monte Carlo algorithm for estimating conditional probabilities based on sampling from an AND/OR tree for ProbLog, a general-purpose probabilistic logic programming language. We propose a parameterizable proposal distribution that generates the next sample in the Markov chain by probabilistically traversing the AND/OR tree from its root, which holds the evidence, to the leaves. An empirical evaluation on several different applications illustrates the advantages of our algorithm.