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@InProceedings{WarnquistKvarnstromDoherty:IBLAO:ECAI,
  author = 	 {Håkan Warnquist and Jonas Kvarnstr\"om and Patrick Doherty},
  title = 	 {Iterative Bounding {LAO*}},
  OPTcrossref =  {},
  OPTkey = 	 {},
  booktitle =	 {Proceedings of the 19th European Conference on Artificial Intelligence ({ECAI})},
  OPTpages = 	 {},
  year =	 {2010},
  OPTeditor = 	 {},
  OPTvolume = 	 {},
  OPTnumber = 	 {},
  OPTseries = 	 {},
  address =	 {Lisbon, Portugal},
  month =	 aug,
  OPTorganization = {},
  OPTpublisher = {},
  OPTnote =	 {Accepted for publication.},
  OPTannote = 	 {},

  projects = {apd ceniit},
  confurl = {http://ecai2010.appia.pt/},
  url = {http://www.ida.liu.se/~jonkv/papers/iterative-bounding-lao-ecai2010.pdf},
  
abstract = { Iterative Bounding LAO* is a new algorithm for
                  epsilon-optimal probabilistic planning problems
                  where an absorbing goal state should be reached at a
                  minimum expected cost from a given initial
                  state. The algorithm is based on the LAO* algorithm
                  for finding optimal solutions in cyclic AND/OR
                  graphs. The new algorithm uses two heuristics, one
                  upper bound and one lower bound of the optimal
                  cost. The search is guided by the lower bound as in
                  LAO*, while the upper bound is used to prune search
                  branches. The algorithm has a new mechanism for
                  expanding search nodes and while maintaining the
                  error bounds, it may use weighted heuristics to
                  reduce the size of the explored search space. In
                  empirical tests on benchmark problems, Iterative
                  Bounding LAO* expands fewer search nodes compared to
                  the state of the art RTDP variants that also use
                  two-sided bounds.  }
}

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