Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
General theory of cumulative inference
Proceedings of the 2nd international workshop on Non-monotonic reasoning
From statistical knowledge bases to degrees of belief
Artificial Intelligence
Combining probabilistic logic programming with the power of maximum entropy
Artificial Intelligence - Special issue on nonmonotonic reasoning
Machine Learning
Random worlds and maximum entropy
Journal of Artificial Intelligence Research
Conditionals in nonmonotonic reasoning and belief revision: considering conditionals as agents
Conditionals in nonmonotonic reasoning and belief revision: considering conditionals as agents
Coherent knowledge processing at maximum entropy by spirit
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Probabilistic logics in expert systems: approaches, implementations, and applications
DEXA'11 Proceedings of the 22nd international conference on Database and expert systems applications - Volume Part I
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
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
Combining subjective probabilities and data in training markov logic networks
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part I
Transactions on Large-Scale Data- and Knowledge-Centered Systems VI
Quasi conjunction, quasi disjunction, t-norms and t-conorms: Probabilistic aspects
Information Sciences: an International Journal
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This paper presents and compares approaches for reasoning with relational probabilistic conditionals, i. e. probabilistic conditionals in a restricted first-order environment. It is well-known that conditionals play a crucial role for default reasoning, however, most formalisms are based on propositional conditionals, which restricts their expressivity. The formalisms discussed in this paper are relational extensions of a propositional conditional logic based on the principle of maximum entropy. We show how this powerful principle can be used in different ways to realize model-based inference relations for first-order probabilistic knowledge bases. We illustrate and compare the different approaches by applying them to several benchmark examples, and we evaluate each approach with respect to properties adopted from default reasoning. We also compare our approach to Bayesian logic programs (BLPs) from the field of statistical relational learning which focuses on the combination of probabilistic reasoning and relational knowledge representation as well.