We propose a new particle filter that incorporates
a model of costs when generating particles. The approach is motivated by
the observation that the costs of accidentally not tracking hypotheses
might be significant in some areas of state space, and irrelevant in others.
By incorporating a cost model into particle filtering, states that are
more critical to the system performance are more likely to be tracked.
Automatic calculation of the cost model is implemented using an MDP value
function calculation that estimates the value of tracking a particular
state. Experiments in two mobile robot domains illustrate the appropriateness
of the approach.