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The VRDP
algorithm [80] enables conventional dynamic programming to
be performed in real-valued multivariate state-spaces where
straightforward discretization would fall prey to the curse of
dimensionality. A *kd*-tree (similar to a decision tree) is used to
partition state space into coarse regions. The coarse regions are
refined into detailed regions, but only in parts of the state space
which are predicted to be important. This notion of importance is
obtained by running ``mental trajectories'' through state space. This
algorithm proved effective on a number of problems for which full
high-resolution arrays would have been impractical. It has the
disadvantage of requiring a guess at an initially valid trajectory
through state-space.

*Leslie Pack Kaelbling *

Wed May 1 13:19:13 EDT 1996