Backjump-based backtracking for constraint satisfaction problems
Artificial Intelligence
Simulation and the Monte Carlo Method
Simulation and the Monte Carlo Method
Lifted search engines for satisfiability
Lifted search engines for satisfiability
Machine Learning
MEBN: A language for first-order Bayesian knowledge bases
Artificial Intelligence
Extending Markov Logic to Model Probability Distributions in Relational Domains
KI '07 Proceedings of the 30th annual German conference on Advances in Artificial Intelligence
Mixed deterministic and probabilistic networks
Annals of Mathematics and Artificial Intelligence
Sound and efficient inference with probabilistic and deterministic dependencies
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Model-theoretic expressivity analysis
Probabilistic inductive logic programming
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
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With Bayesian logic networks (BLNs), we present a practical representation formalism for statistical relational knowledge. Based on the concept of mixed networks with probabilistic and deterministic constraints, BLNs combine the probabilistic semantics of (relational) Bayesian networks with constraints in first-order logic. In practical applications, efficient inference in statistical relational models such as BLNs is a key concern. Motivated by the inherently mixed nature of models instantiated from BLNs, we investigate two novel importance sampling methods: The first combines backward simulation, i.e. sampling backward from the evidence, with systematic search, while the second explores the possibility of recording abstract constraints during the search for samples.