Title: Improper Filter Reduction,
Abstract: Combinatorial filters have been the subject of increasing interest from the
robotics community in recent years. This paper considers automatic reduction of
combinatorial filters to a given size, even if that reduction necessitates
changes to the filter's behavior. We introduce an algorithmic problem called
improper filter reduction, in which the input is a combinatorial filter F along
with an integer k representing the target size. The output is another
combinatorial filter F' with at most k states, such that the difference in
behavior between F and F' is minimal. We present two metrics for measuring the
distance between pairs of filters, describe dynamic programming algorithms for
computing these distances, and show that improper filter reduction is NP-hard
under these metrics. We then describe two heuristic algorithms for improper
filter reduction, one greedy sequential approach, and one randomized global
approach based on prior work on weighted improper graph coloring. We have
implemented these algorithms and analyze the results of three sets of
experiments.