  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.
