@InProceedings{lang09icml,
  TITLE     = {Approximate Inference for Planning in Stochastic Relational Worlds},
  AUTHOR    = {Tobias Lang and Marc Toussaint},
  BOOKTITLE = {Proc.~of the Int.~Conf.~on Machine Learning (ICML)},
  YEAR      = {2009},
  PAGES     = {585--592},
  MONTH     = {June},
  PDFURL    = {http://www.ml.cs.tu-berlin.de/~lang/pub/lang09icml.pdf},
  ABSTRACT  = {Relational world models that can be learned from experience
in stochastic domains have received significant attention recently. However,
efficient planning using these models remains a major issue.
We propose to convert learned noisy probabilistic
relational rules into a structured dynamic Bayesian network
representation. Predicting the effects of action sequences using approximate
inference allows for planning in complex worlds.
We evaluate the effectiveness of our approach for online planning in a 3D
simulated blocksworld with an articulated manipulator and realistic physics.
Empirical results show that our method can solve problems where existing methods
fail.},
}