Principles of database and knowledge-base systems, Vol. I
Principles of database and knowledge-base systems, Vol. I
A model for reasoning about persistence and causation
Computational Intelligence
Machine Learning - special issue on inductive logic programming
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Machine Learning for Sequential Data: A Review
Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Context-based vision system for place and object recognition
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Reconstructing force-dynamic models from video sequences
Artificial Intelligence
EMNLP '00 Proceedings of the 2000 Joint SIGDAT conference on Empirical methods in natural language processing and very large corpora: held in conjunction with the 38th Annual Meeting of the Association for Computational Linguistics - Volume 13
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
A simple-transition model for relational sequences
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Learning the behavior model of a robot
Autonomous Robots
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We present a trainable sequential-inference technique for processes with large state and observation spaces and relational structure. Our method assumes "reliable observations", i.e. that each process state persists long enough to be reliably inferred from the observations it generates. We introduce the idea of a "state-inference function" (from observation sequences to underlying hidden states) for representing knowledge about a process and develop an efficient sequential-inference algorithm, utilizing this function, that is correct for processes that generate reliable observations consistent with the state-inference function. We describe a representation for state-inference functions in relational domains and give a corresponding supervised learning algorithm. Experiments, in relational video interpretation, show that our technique provides significantly improved accuracy and speed relative to a variety of recent, hand-coded, non-trainable systems.