A temporally abstracted Viterbi algorithm
  Hierarchical problem abstraction, when applicable, may offer exponential
reductions in computational complexity. Previous work on coarse-to-fine dynamic
programming (CFDP) has demonstrated this possibility using state abstraction to
speed up the Viterbi algorithm. In this paper, we show how to apply temporal
abstraction to the Viterbi problem. Our algorithm uses bounds derived from
analysis of coarse timescales to prune large parts of the state trellis at
finer timescales. We demonstrate improvements of several orders of magnitude
over the standard Viterbi algorithm, as well as significant speedups over CFDP,
for problems whose state variables evolve at widely differing rates.
