Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
A computational scheme for reasoning in dynamic probabilistic networks
UAI '92 Proceedings of the eighth conference on Uncertainty in Artificial Intelligence
Reasoning about noisy sensors and effectors in the situation calculus
Artificial Intelligence
An Introduction to Variational Methods for Graphical Models
Machine Learning
Probabilistic Situation Calculus
Annals of Mathematics and Artificial Intelligence
PEGASUS: A policy search method for large MDPs and POMDPs
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Efficient Approximation for Triangulation of Minimum Treewidth
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
Dynamic bayesian networks: representation, inference and learning
Dynamic bayesian networks: representation, inference and learning
Algorithms and Complexity Results for #SAT and Bayesian Inference
FOCS '03 Proceedings of the 44th Annual IEEE Symposium on Foundations of Computer Science
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Learning partially observable deterministic action models
Journal of Artificial Intelligence Research
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Stochastic filtering is the problem of estimating the state of a dynamic system after time passes and given partial observations. It is fundamental to automatic tracking, planning, and control of real-world stochastic systems such as robots, programs, and autonomous agents. This paper presents a novel sampling-based filtering algorithm. Its expected error is smaller than sequential Monte Carlo sampling techniques given a fixed number of samples, as we prove and show empirically. It does so by sampling deterministic action sequences and then performing exact filtering on those sequences. These results are promising for applications in stochastic planning, natural language processing, and robot control.