Operations Research
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
Predicting and explaining success and task duration in the Phoenix planner
Proceedings of the first international conference on Artificial intelligence planning systems
A computational scheme for reasoning in dynamic probabilistic networks
UAI '92 Proceedings of the eighth conference on Uncertainty in Artificial Intelligence
An algorithm for probabilistic planning
Artificial Intelligence - Special volume on planning and scheduling
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
Tractable inference for complex stochastic processes
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Probabilistic temporal reasoning with endogenous change
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Stochastic simulation algorithms for dynamic probabilistic networks
UAI'95 Proceedings of the Eleventh 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
A scheme for approximating probabilistic inference
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Optimal Monte Carlo estimation of belief network inference
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Probabilistic state-dependent grammars for plan recognition
UAI'00 Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence
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The dynamic execution of plans in uncertain domains requires the ability to infer likely current and future world states from past observations. We cast this task as inference on Dynamic Belief Networks (DBNs) but the resulting networks are difficult to solve with exact methods. We investigate and extend simulation algorithms for approximate inference on Bayesian networks and propose a new algorithm, called Rewind/Replay, for generating a set of simulations weighted by their likelihood given past observations. We validate our algorithm on a DBN containing thousands of variables, which models the spread of wildfire.