Artificial Intelligence
A logic for reasoning about probabilities
Information and Computation - Selections from 1988 IEEE symposium on logic in computer science
Reasoning about knowledge and probability
Journal of the ACM (JACM)
The uncertain reasoner's companion: a mathematical perspective
The uncertain reasoner's companion: a mathematical perspective
Representation and extraction of information by probabilistic logic
Information Systems
Characterizing the principle of minimum cross-entropy within a conditional-logical framework
Artificial Intelligence
On first-order conditional logics
Artificial Intelligence
Reasoning about Uncertainty
Combining probabilistic logic programming with the power of maximum entropy
Artificial Intelligence - Special issue on nonmonotonic reasoning
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
SUM '08 Proceedings of the 2nd international conference on Scalable Uncertainty Management
Using Histograms to Better Answer Queries to Probabilistic Logic Programs
ICLP '09 Proceedings of the 25th International Conference on Logic Programming
First-order probabilistic inference
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Conditionals in nonmonotonic reasoning and belief revision: considering conditionals as agents
Conditionals in nonmonotonic reasoning and belief revision: considering conditionals as agents
Relational probabilistic conditional reasoning at maximum entropy
ECSQARU'11 Proceedings of the 11th European conference on Symbolic and quantitative approaches to reasoning with uncertainty
KI'11 Proceedings of the 34th Annual German conference on Advances in artificial intelligence
Transformation rules for first-order probabilistic conditional logic yielding parametric uniformity
KI'11 Proceedings of the 34th Annual German conference on Advances in artificial intelligence
JELIA'12 Proceedings of the 13th European conference on Logics in Artificial Intelligence
Instantiation restrictions for relational probabilistic conditionals
SUM'12 Proceedings of the 6th international conference on Scalable Uncertainty Management
Transactions on Large-Scale Data- and Knowledge-Centered Systems VI
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When extending probabilistic logic to a relational setting, it is desirable to still be able to use efficient inference mechanisms developed for the propositional case. In this paper, we investigate the relational probabilistic conditional logic FO-PCL whose semantics employs the principle of maximum entropy. While in general, this semantics is defined via the ground instances of the rules in an FO-PCL knowledge base R, the maximum entropy model can be computed on the level of rules rather than on the level of instances of the rules if R is parametrically uniform, thus providing lifted inference.We elaborate in detail the reasons precluding R from being parametrically uniform. Based on this investigation, we derive a new syntactic criterion for parametric uniformity and develop an algorithm that transforms any FO-PCL knowledge base R into an equivalent knowledge base R′ that is parametrically uniform.