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Abstract:
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
next to 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.
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