The SLAM project: debugging system software via static analysis
POPL '02 Proceedings of the 29th ACM SIGPLAN-SIGACT symposium on Principles of programming languages
Counterexample-Guided Abstraction Refinement
CAV '00 Proceedings of the 12th International Conference on Computer Aided Verification
Adaptive duplicate detection using learnable string similarity measures
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Database Systems Concepts
Learning the structure of Markov logic networks
ICML '05 Proceedings of the 22nd international conference on Machine learning
Machine Learning
Entity Resolution with Markov Logic
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Bottom-up learning of Markov logic network structure
Proceedings of the 24th international conference on Machine learning
The software model checker Blast: Applications to software engineering
International Journal on Software Tools for Technology Transfer (STTT)
The Calculus of Computation: Decision Procedures with Applications to Verification
The Calculus of Computation: Decision Procedures with Applications to Verification
Memory-efficient inference in relational domains
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Joint inference in information extraction
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Lifted probabilistic inference with counting formulas
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
A general method for reducing the complexity of relational inference and its application to MCMC
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
First-order probabilistic inference
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Lifted first-order probabilistic inference
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
TACAS'08/ETAPS'08 Proceedings of the Theory and practice of software, 14th international conference on Tools and algorithms for the construction and analysis of systems
Tuffy: scaling up statistical inference in Markov logic networks using an RDBMS
Proceedings of the VLDB Endowment
Symbolic optimization with SMT solvers
Proceedings of the 41st ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages
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In statistical relational learning, one is concerned with inferring the most likely explanation (or world) that satisfies a given set of weighted constraints. The weight of a constraint signifies our confidence in the constraint, and the most likely world that explains a set of constraints is simply a satisfying assignment that maximizes the weights of satisfied constraints. The relational learning community has developed specialized solvers (e.g., Alchemy and Tuffy) for such weighted constraints independently of the work on SMT solvers in the verification community. In this paper, we show how to leverage SMT solvers to significantly improve the performance of relational solvers. Constraints associated with a weight of 1 (or 0) are called axioms because they must be satisfied (or violated) by the final assignment. Axioms can create difficulties for relational solvers. We isolate the burden of axioms to SMT solvers and only lazily pass information back to the relational solver. This information can either be a subset of the axioms, or even generalized axioms (similar to predicate generalization in verification). We implemented our algorithm in a tool called Soft-Cegar that out-performs state-of-the-art relational solvers Tuffy and Alchemy over four real-world applications. We hope this work opens the door for further collaboration between relational learning and SMT solvers.