Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
A Machine-Oriented Logic Based on the Resolution Principle
Journal of the ACM (JACM)
Machine Learning
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
MPE and partial inversion in lifted probabilistic variable elimination
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Memory-efficient inference in relational domains
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Discriminative training of Markov logic networks
AAAI'05 Proceedings of the 20th 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
SPOOK: a system for probabilistic object-oriented knowledge representation
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Factor graphs and the sum-product algorithm
IEEE Transactions on Information Theory
Loopy Propagation in a Probabilistic Description Logic
SUM '08 Proceedings of the 2nd international conference on Scalable Uncertainty Management
Just Add Weights: Markov Logic for the Semantic Web
Uncertainty Reasoning for the Semantic Web I
Learning Markov logic network structure via hypergraph lifting
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Max-Margin Weight Learning for Markov Logic Networks
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I
Jointly identifying predicates, arguments and senses using Markov logic
NAACL '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics
PrDB: managing and exploiting rich correlations in probabilistic databases
The VLDB Journal — The International Journal on Very Large Data Bases
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Equipping robot control programs with first-order probabilistic reasoning capabilities
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
First-order mixed integer linear programming
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
Constraint processing in lifted probabilistic inference
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
Bisimulation-based approximate lifted inference
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
Computing query probability with incidence algebras
Proceedings of the twenty-ninth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Plan assessment for autonomous manufacturing as Bayesian inference
KI'10 Proceedings of the 33rd annual German conference on Advances in artificial intelligence
Probabilistic declarative process mining
KSEM'10 Proceedings of the 4th international conference on Knowledge science, engineering and management
Bayesian knowledge corroboration with logical rules and user feedback
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part II
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part II
ILP'09 Proceedings of the 19th international conference on Inductive logic programming
Speeding up inference in statistical relational learning by clustering similar query literals
ILP'09 Proceedings of the 19th international conference on Inductive logic programming
Tuffy: scaling up statistical inference in Markov logic networks using an RDBMS
Proceedings of the VLDB Endowment
Learning statistical models from relational data
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
Logic, probability and computation: foundations and issues of statistical relational AI
LPNMR'11 Proceedings of the 11th international conference on Logic programming and nonmonotonic reasoning
Constraint propagation for efficient inference in Markov logic
CP'11 Proceedings of the 17th international conference on Principles and practice of constraint programming
Decision-theoretic planning with generalized first-order decision diagrams
Artificial Intelligence
KI'11 Proceedings of the 34th Annual German conference on Advances in artificial intelligence
Efficient sequential clamping for lifted message passing
KI'11 Proceedings of the 34th Annual German conference on Advances in artificial intelligence
Dyna: extending datalog for modern AI
Datalog'10 Proceedings of the First international conference on Datalog Reloaded
Multi-evidence lifted message passing, with application to PageRank and the Kalman filter
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
Lifted probabilistic inference by first-order knowledge compilation
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Rethinking cognitive architecture via graphical models
Cognitive Systems Research
A discriminative hierarchical model for fast coreference at large scale
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1
Efficient graph kernels by randomization
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part I
Lifted online training of relational models with stochastic gradient methods
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part I
Programming with personalized pagerank: a locally groundable first-order probabilistic logic
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Statistical relational data integration for information extraction
RW'13 Proceedings of the 9th international conference on Reasoning Web: semantic technologies for intelligent data access
Lifted variable elimination: decoupling the operators from the constraint language
Journal of Artificial Intelligence Research
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Unifying first-order logic and probability is a long-standing goal of AI, and in recent years many representations combining aspects of the two have been proposed. However, inference in them is generally still at the level of propositional logic, creating all ground atoms and formulas and applying standard probabilistic inference methods to the resulting network. Ideally, inference should be lifted as in first-order logic, handling whole sets of indistinguishable objects together, in time independent of their cardinality. Poole (2003) and Braz et al. (2005, 2006) developed a lifted version of the variable elimination algorithm, but it is extremely complex, generally does not scale to realistic domains, and has only been applied to very small artificial problems. In this paper we propose the first lifted version of a scalable probabilistic inference algorithm, belief propagation (loopy or not). Our approach is based on first constructing a lifted network, where each node represents a set of ground atoms that all pass the same messages during belief propagation. We then run belief propagation on this network. We prove the correctness and optimality of our algorithm. Experiments show that it can greatly reduce the cost of inference.