  The ways in which an agent's actions affect the world can often be modeled
compactly using a set of relational probabilistic planning rules. This paper
addresses the problem of learning such rule sets for multiple related tasks. We
take a hierarchical Bayesian approach, in which the system learns a prior
distribution over rule sets. We present a class of prior distributions
parameterized by a rule set prototype that is stochastically modified to
produce a task-specific rule set. We also describe a coordinate ascent
algorithm that iteratively optimizes the task-specific rule sets and the prior
distribution. Experiments using this algorithm show that transferring
information from related tasks significantly reduces the amount of training
data required to predict action effects in blocks-world domains.
