  We present a representation for describing transition models in complex
uncertain domains using relational rules. For any action, a rule selects a set
of relevant objects and computes a distribution over properties of just those
objects in the resulting state given their properties in the previous state. An
iterative greedy algorithm is used to construct a set of deictic references
that determine which objects are relevant in any given state. Feed-forward
neural networks are used to learn the transition distribution on the relevant
objects' properties. This strategy is demonstrated to be both more versatile
and more sample efficient than learning a monolithic transition model in a
simulated domain in which a robot pushes stacks of objects on a cluttered
table.
