@ARTICLE{lang-toussaint-10jair,
    author = {Tobias Lang and Marc Toussaint},
    title = {Planning with Noisy Probabilistic Relational Rules},
    journal = {Journal of Artificial Intelligence Research},
    year = {2010},
    volume = {39},
    pages = {1-49},
    pdfurl = {http://www.jair.org/media/3093/live-3093-5172-jair.pdf},
    abstract = {Noisy probabilistic relational rules are a promising world
model representation for several reasons. They are compact and generalize over
world instantiations. They are usually interpretable and they can be learned
effectively from the action experiences in complex worlds. We investigate
reasoning with such rules
in grounded relational domains. Our algorithms exploit the compactness of rules
for efficient and flexible decision-theoretic planning. As a first approach, we
combine these rules with the Upper Confidence Bounds applied to Trees (UCT)
algorithm based on look-ahead trees. Our second approach converts these rules
into a structured dynamic Bayesian network representation and predicts the
effects of action sequences using approximate inference and beliefs over world
states. We evaluate the effectiveness of our approaches for planning in a 
simulated complex 3D robot manipulation scenario with an articulated manipulator
and realistic physics and in domains of the probabilistic planning competition.
Empirical results show that our methods can solve problems where existing
methods fail.},
}