Principles of database and knowledge-base systems, Vol. I
Principles of database and knowledge-base systems, Vol. I
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
A model for reasoning about persistence and causation
Computational Intelligence
Physics-based visual understanding
Computer Vision and Image Understanding - Special issue on physics-based modeling and reasoning in computer vision
Machine Learning - special issue on inductive logic programming
Learning Logical Definitions from Relations
Machine Learning
A Maximum-Likelihood Approach to Visual Event Classification
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume II - Volume II
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
Visual Event Classification via Force Dynamics
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Coupled hidden Markov models for complex action recognition
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Action Recognition Using Probabilistic Parsing
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Towards the Computational Perception of Action
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and 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
Learning relational probability trees
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Reconstructing force-dynamic models from video sequences
Artificial Intelligence
Machine Learning
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
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 4
Journal of Artificial Intelligence Research
Grounding the lexical semantics of verbs in visual perception using force dynamics and event logic
Journal of Artificial Intelligence Research
Learning probabilistic relational models
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Dynamic probabilistic relational models
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Propositional non-monotonic reasoning and inconsistency in symmetric neural networks
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 1
A simple-transition model for relational sequences
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Discriminative probabilistic models for relational data
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
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We present a trainable sequential-inference technique for processes with large state and observation spaces and relational structure. We apply our technique to the problem of force-dynamic state inference from video, which is a critical component of the LEONARD [J.M. Siskind, Grounding lexical semantics of verbs in visual perception using force dynamics and event logic, Journal of Artificial Intelligence Research 15 (2001) 31-90] visual-event recognition system. LEONARD uses event definitions that are grounded in force-dynamic primitives-making robust and efficient force-dynamic inference critical to good performance. Our sequential-inference method assumes ''reliable observations'', i.e., that each process state (e.g., force-dynamic state) persists long enough to be reliably inferred from the observations (e.g., video frames) 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. Our experiments in force-dynamic state inference show that our technique provides significantly improved accuracy and speed relative to a variety of recent, hand-coded, non-trainable systems, and a trainable system based on probabilistic modeling.