An Introduction to Variational Methods for Graphical Models
Machine Learning
Approximating state estimation in multiagent settings using particle filters
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
A particle filtering based approach to approximating interactive POMDPs
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
A variational approximation for Bayesian networks with discrete and continuous latent variables
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
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We present a new method for carrying out state estimation in multi-agent settings that are characterized by continuous or large discrete state spaces. State estimation in multiagent settings involves updating an agent's belief over the physical states and the space of other agents' models. We factor out the models of the other agents and update the agent's belief over these models, as exactly as possible. Simultaneously, we sample particles from the distribution over the large physical state space and project the particles in time.