A logic-based calculus of events
New Generation Computing
Foundations of logic programming; (2nd extended ed.)
Foundations of logic programming; (2nd extended ed.)
Transforming abductive logic programs to disjunctive programs
ICLP'93 Proceedings of the tenth international conference on logic programming on Logic programming
Machine Learning
Inductive Logic Programming: Techniques and Applications
Inductive Logic Programming: Techniques and Applications
Answer set programming and plan generation
Artificial Intelligence
Probabilistic Logic Programs and their Semantics
Proceedings of the First Russian Conference on Logic Programming
Nonmonotonic Inductive Logic Programming
LPNMR '01 Proceedings of the 6th International Conference on Logic Programming and Nonmonotonic Reasoning
ECP '99 Proceedings of the 5th European Conference on Planning: Recent Advances in AI Planning
Learning the structure of Markov logic networks
ICML '05 Proceedings of the 22nd international conference on Machine learning
Machine Learning
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
Extracting Requirements from Scenarios with ILP
Inductive Logic Programming
Parameter Learning in Probabilistic Databases: A Least Squares Approach
ECML PKDD '08 Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I
On the Efficient Execution of ProbLog Programs
ICLP '08 Proceedings of the 24th International Conference on Logic Programming
Discriminative training of Markov logic networks
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
Learning planning rules in noisy stochastic worlds
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
Universal plans for reactive robots in unpredictable environments
IJCAI'87 Proceedings of the 10th international joint conference on Artificial intelligence - Volume 2
Evaluating abductive hypotheses using an EM algorithm on BDDs
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Learning structure and parameters of stochastic logic programs
ILP'02 Proceedings of the 12th international conference on Inductive logic programming
ILP'10 Proceedings of the 20th international conference on Inductive logic programming
Learning revised models for planning in adaptive systems
Proceedings of the 2013 International Conference on Software Engineering
Learning from interpretation transition
Machine Learning
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We propose here a novel approach to rule learning in probabilistic nonmonotonic domains in the context of answer set programming. We used the approach to update the knowledge base of an agent based on observations. To handle the probabilistic nature of our observation data, we employ parameter estimation to find the probabilities associated with each of these atoms and consequently with rules. The outcome is the set of rules which have the greatest probability of entailing the observations. This ultimately improves tolerance of noisy data compared to traditional inductive logic programming techniques. We illustrate the benefits of the approach by applying it to a planning problem in which the involved agent requires both nonmonotonicity and tolerance of noisy input.