Region-Based Incremental Pruning for POMDPs
  We present a major improvement to the incremental pruning algorithm for
solving partially observable Markov decision processes. Our technique targets
the cross-sum step of the dynamic programming (DP) update, a key source of
complexity in POMDP algorithms. Instead of reasoning about the whole belief
space when pruning the cross-sums, our algorithm divides the belief space into
smaller regions and performs independent pruning in each region. We evaluate
the benefits of the new technique both analytically and experimentally, and
show that it produces very significant performance gains. The results
contribute to the scalability of POMDP algorithms to domains that cannot be
handled by the best existing techniques.
