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Recent research has examined algorithms to minimize robots' resource footprints. The class of combinatorial filters (discrete variants of widely-used probabilistic estimators) has been studied and methods for reducing their space requirements introduced. This paper extends existing combinatorial filters by introducing a natural generalization that we dub cover combinatorial filters. In addressing the new -- but still NP-complete -- problem of minimization of cover filters, this paper shows thatarXiv:2002.07153v4 fatcat:o4hljywrijguzegzvrviuyfmru