Inference in resource-constrained environments is a challenging
problem where optimal solutions are generally intractable. For
example, state estimation in distributed sensor networks presents a
fundamental trade-off between the value of information contained in a
distributed set of measurements versus the resources expended to
acquire them, fuse them into a model of uncertainty, and then transmit
the resulting model. Approximate approaches have been proposed that
treat a subset of these issues; however, the approaches are indirect
and usually consider at most one or two future time steps. I will
discuss a method which enables long time-horizon sensor planning in
the context of state estimation with a distributed sensor network. The
approach integrates the value of information discounted by resource
expenditures over a rolling time horizon. Simulation results
demonstrate that the resulting algorithm can provide similar
estimation performance to that of the common "most informative sensor
selection" method for a fraction of the resource expenditures.
Additionally, I will present performance guarantees bounding the
performance difference between optimal and approximate methods of
measurement selection for information-driven Bayesian filtering. Note
that the structure of these bounds applies to a variety of machine
learning problems including active learning and inference in graphical
models. Furthermore, it can be shown that the bounds are tight.
This is joint work with Jason Williams
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