Uncertainty in Artificial Intelligence
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A temporally abstracted Viterbi algorithm
Shaunak Chatterjee, Stuart Russell
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.
Pages: 96-104
PS Link:
PDF Link: /papers/11/p96-chatterjee.pdf
AUTHOR = "Shaunak Chatterjee and Stuart Russell",
TITLE = "A temporally abstracted Viterbi algorithm",
BOOKTITLE = "Proceedings of the Twenty-Seventh Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-11)",
ADDRESS = "Corvallis, Oregon",
YEAR = "2011",
PAGES = "96--104"

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