Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
The uncertain reasoner's companion: a mathematical perspective
The uncertain reasoner's companion: a mathematical perspective
Characterizing the principle of minimum cross-entropy within a conditional-logical framework
Artificial Intelligence
On first-order conditional logics
Artificial Intelligence
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
Lifted first-order belief propagation
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
Equipping robot control programs with first-order probabilistic reasoning capabilities
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Probabilistic inductive logic programming
Probabilistic inductive logic programming
Model-theoretic expressivity analysis
Probabilistic inductive logic programming
Markov Logic: An Interface Layer for Artificial Intelligence
Markov Logic: An Interface Layer for Artificial Intelligence
Relational probabilistic conditional reasoning at maximum entropy
ECSQARU'11 Proceedings of the 11th European conference on Symbolic and quantitative approaches to reasoning with uncertainty
The relationship of the logic of big-stepped probabilities to standard probabilistic logics
FoIKS'10 Proceedings of the 6th international conference on Foundations of Information and Knowledge Systems
On lifted inference for a relational probabilistic conditional logic with maximum entropy semantics
FoIKS'12 Proceedings of the 7th international conference on Foundations of Information and Knowledge Systems
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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In the past ten years, the areas of probabilistic inductive logic programming and statistical relational learning put forth a large collection of approaches to combine relational representations of knowledge with probabilistic reasoning. Here, we develop a series of evaluation and comparison criteria for those approaches and focus on the point of view of knowledge representation and reasoning. These criteria address abstract demands such as language aspects, the relationships to propositional probabilistic and first-order logic, and their treatment of information on individuals. We discuss and illustrate the criteria thoroughly by applying them to several approaches to probabilistic relational knowledge representation, in particular, Bayesian logic programs, Markov logic networks, and three approaches based on the principle of maximum entropy.