A model for reasoning about persistence and causation
Computational Intelligence
Elements of information theory
Elements of information theory
A computational scheme for reasoning in dynamic probabilistic networks
UAI '92 Proceedings of the eighth conference on Uncertainty in Artificial Intelligence
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
The BATmobile: towards a Bayesian automated taxi
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Tractable inference for complex stochastic processes
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
A Probabilistic Approach to Collaborative Multi-Robot Localization
Autonomous Robots
Force deployment analysis with generalized grammar
Information Fusion
Value-function approximations for partially observable Markov decision processes
Journal of Artificial Intelligence Research
Thin junction tree filters for simultaneous localization and mapping
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Discovering the hidden structure of complex dynamic systems
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Factored particles for scalable monitoring
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Sufficiency, separability and temporal probabilistic models
UAI'01 Proceedings of the Seventeenth conference on Uncertainty in artificial intelligence
Efficient planning for factored infinite-horizon DEC-POMDPs
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume One
Distributed data association in smart camera networks using belief propagation
ACM Transactions on Sensor Networks (TOSN)
| Hi-index | 0.00 |
Consider the problem of monitoring the state of a complex dynamic system, and predicting its future evolution. Exact algorithms for this task typically maintain a belief state, or distribution over the states at some point in time. Unfortunately, these algorithms fail when applied to complex processes such as those represented as dynamic Bayesian networks (DBNs), as the representation of the belief state grows exponentially with the size of the process. In (Boyen & Koller 1998), we recently proposed an efficient approximate tracking algorithm that maintains an approximate belief state that has a compact representation as a set of independent factors. Its performance depends on the error introduced by approximating a belief state of this process by a factored one. We informally argued that this error is low if the interaction between variables in the processes is "weak". In this paper, we give formal information-theoretic definitions for notions such as weak interaction and sparse interaction of processes. We use these notions to analyze the conditions under which the error induced by this type of approximation is small. We demonstrate several cases where our results formally support intuitions about strength of interaction.