Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Collaborative plans for complex group action
Artificial Intelligence
Understanding belief propagation and its generalizations
Exploring artificial intelligence in the new millennium
A robust architecture for distributed inference in sensor networks
IPSN '05 Proceedings of the 4th international symposium on Information processing in sensor networks
Loopy belief propagation for approximate inference: an empirical study
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Nonparametric belief propagation for self-localization of sensor networks
IEEE Journal on Selected Areas in Communications
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We propose Markov random fields (MRFs) as a probabilistic mathematical model for unifying approaches to multi-robot coordination or, more specifically, distributed action selection. The MRF model is well-suited to domains in which the joint probability over latent (action) and observed (perceived) variables can be factored into pairwise interactions between these variables. Specifically, these interactions occur through functions that evaluate "local evidence" between an observed and latent variable and "compatibility" between a pair of latent variables. For multi-robot coordination, we cast local evidence functions as the computation for an individual robot's action selection from its local observations and compatibility as the dependence in action selection between a pair of robots. We describe how existing methods for multi-robot coordination (or at least a non-exhaustive subset) fit within an MRF-based model and how they conceptually unify. Further, we offer belief propagation on a multi-robot MRF as a novel approach to distributed robot action selection.