Operations Research
A model for reasoning about persistence and causation
Computational Intelligence
A computational scheme for reasoning in dynamic probabilistic networks
UAI '92 Proceedings of the eighth conference on Uncertainty in Artificial Intelligence
The data association problem when monitoring robot vehicles using dynamic belief networks
ECAI '92 Proceedings of the 10th European conference on Artificial intelligence
Approximating probabilistic inference in Bayesian belief networks is NP-hard
Artificial Intelligence
Automatic symbolic traffic scene analysis using belief networks
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
Simulation Approaches to General Probabilistic Inference on Belief Networks
UAI '89 Proceedings of the Fifth Annual Conference on Uncertainty in Artificial Intelligence
Weighing and Integrating Evidence for Stochastic Simulation in Bayesian Networks
UAI '89 Proceedings of the Fifth Annual Conference on Uncertainty in Artificial Intelligence
"Go with the winners" algorithms
SFCS '94 Proceedings of the 35th Annual Symposium on Foundations of Computer Science
The BATmobile: towards a Bayesian automated taxi
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Factorial Hidden Markov Models
Machine Learning - Special issue on learning with probabilistic representations
Learning agents for uncertain environments (extended abstract)
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Structured representation of complex stochastic systems
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Proceedings of the 1999 ACM symposium on Applied computing
Monte Carlo localization: efficient position estimation for mobile robots
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Simulation-based inference for plan monitoring
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
An Introduction to Variational Methods for Graphical Models
Machine Learning
An introduction to hidden Markov models and Bayesian networks
Hidden Markov models
Probabilistic Methods for Finding People
International Journal of Computer Vision
A Probabilistic Approach to Collaborative Multi-Robot Localization
Autonomous Robots
International Journal of Computer Vision - Special issue on statistical and computational theories of vision: Part II
How Does CONDENSATION Behave with a Finite Number of Samples?
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part I
Arc Weights for Approximate Evaluation of Dynamic Belief Networks
AI '99 Proceedings of the 12th Australian Joint Conference on Artificial Intelligence: Advanced Topics in Artificial Intelligence
Harnessing Models of Users' Goals to Mediate Clarification Dialog in Spoken Language Systems
UM '01 Proceedings of the 8th International Conference on User Modeling 2001
Evidential Reasoning for Object Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Addressing the Problems of Bayesian Network Classification of Video Using High-Dimensional Features
IEEE Transactions on Knowledge and Data Engineering
Case-factor diagrams for structured probabilistic modeling
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Extracting Places and Activities from GPS Traces Using Hierarchical Conditional Random Fields
International Journal of Robotics Research
Interacting and Annealing Particle Filters: Mathematics and a Recipe for Applications
Journal of Mathematical Imaging and Vision
Case-factor diagrams for structured probabilistic modeling
Journal of Computer and System Sciences
Reasoning with recursive loops under the PLP framework
ACM Transactions on Computational Logic (TOCL)
International Journal of Autonomous and Adaptive Communications Systems
Force deployment analysis with generalized grammar
Information Fusion
Factored reasoning for monitoring dynamic team and goal formation
Information Fusion
Optimising dynamic graphical models for video content analysis
Computer Vision and Image Understanding
CamShift guided particle filter for visual tracking
Pattern Recognition Letters
Value-function approximations for partially observable Markov decision processes
Journal of Artificial Intelligence Research
Finding approximate POMDP solutions through belief compression
Journal of Artificial Intelligence Research
Policy recognition in the abstract hidden Markov model
Journal of Artificial Intelligence Research
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Knowledge representation for stochastic decision processes
Artificial intelligence today
Robust mobile robot localization in highly non-static environments
Autonomous Robots
Learning the behavior model of a robot
Autonomous Robots
SUM'11 Proceedings of the 5th international conference on Scalable uncertainty management
Model based Bayesian exploration
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
A general algorithm for approximate inference and its application to hybrid bayes nets
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Rao-blackwellised particle filtering for dynamic Bayesian networks
UAI'00 Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence
Conversation as action under uncertainty
UAI'00 Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence
Monte Carlo inference via greedy importance sampling
UAI'00 Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence
Value-directed sampling methods for monitoring POMDPs
UAI'01 Proceedings of the Seventeenth conference on Uncertainty in artificial intelligence
Tractable inference for complex stochastic processes
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Learning the structure of dynamic probabilistic networks
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Structured arc reversal and simulation of dynamic probabilistic networks
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Time-critical action: representations and application
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
ICIAR'06 Proceedings of the Third international conference on Image Analysis and Recognition - Volume Part II
Review: learning bayesian networks: Approaches and issues
The Knowledge Engineering Review
Deriving a stationary dynamic bayesian network from a logic program with recursive loops
ILP'05 Proceedings of the 15th international conference on Inductive Logic Programming
On accurate localization and uncertain sensors
International Journal of Intelligent Systems
Aggregating web offers to determine product prices
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Expert Systems with Applications: An International Journal
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Stochastic simulation algorithms such as likelihood weighting often give fast, accurate approximations to posterior probabilities in probabilistic networks, and are the methods bf choice for very large networks. Unfortunately, the special characteristics of dynamic probabilistic networks (DPNs), which are used to represent stochastic temporal processes, mean that standard simulation algorithms perform very poorly. In essence, the simulation trials diverge further and further from reality as the process is observed over time. In this paper, we present simulation algorithms that use the evidence observed at each time step to push the set of trials back towards reality. The first algorithm, "evidence reversal" (ER) restructures each time slice of the DPN so that the evidence nodes for the slice become ancestors of the state variables. The second algorithm, called "survival of the fittest" sampling (SOF), "repopulates" the set of trials at each time step using a stochastic reproduction rate weighted by the likelihood of the evidence according to each trial. We compare the performance of each algorithm with likelihood weighting on the original network, and also investigate the benefits of combining the ER and SOF methods. The ER/SOF combination appears to maintain bounded error independent of the number of time steps in the simulation.