On the complexity of the maximum satisfiability problem for Horn formulas
Information Processing Letters
Machine Learning - special issue on inductive logic programming
Bucket elimination: a unifying framework for reasoning
Artificial Intelligence
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Efficient graph-based energy minimization methods in computer vision
Efficient graph-based energy minimization methods in computer vision
Dynamic bayesian networks: representation, inference and learning
Dynamic bayesian networks: representation, inference and learning
ACM SIGKDD Explorations Newsletter
Reconstructing force-dynamic models from video sequences
Artificial Intelligence
Relational sequential inference with reliable observations
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Learning models and formulas of a temporal event logic
Learning models and formulas of a temporal event logic
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Grounding the lexical semantics of verbs in visual perception using force dynamics and event logic
Journal of Artificial Intelligence Research
Dynamic probabilistic relational models
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Discriminative probabilistic models for relational data
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Sequential inference with reliable observations: learning to construct force-dynamic models
Artificial Intelligence
A Simple Model for Sequences of Relational State Descriptions
ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
Real-time detection of task switches of desktop users
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Sequential inference with reliable observations: Learning to construct force-dynamic models
Artificial Intelligence
Don't fear optimality: sampling for probabilistic-logic sequence models
ILP'09 Proceedings of the 19th international conference on Inductive logic programming
Predicate Logic Based Image Grammars for Complex Pattern Recognition
International Journal of Computer Vision
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We use "nearly sound" logical constraints to infer hidden states of relational processes. We introduce a simple-transition cost model, which is parameterized by weighted constraints and a statetransition cost. Inference for this model, i.e. finding a minimum-cost state sequence, reduces to a single-state minimization (SSM) problem. For relational Horn constraints, we give a practical approach to SSM based on logical reasoning and bounded search. We present a learning method that discovers relational constraints using CLAUDIEN [De Raedt and Dehaspe, 1997] and then tunes their weights using perceptron updates. Experiments in relational video interpretation show that our learned models improve on a variety of competitors.