  Planning can often be simpli ed by decomposing the task into smaller tasks
arranged hierarchically. Charlin et al. [4] recently showed that the hierarchy
discovery problem can be framed as a non-convex optimization problem. However,
the inherent computational di culty of solving such an optimization problem
makes it hard to scale to realworld problems. In another line of research,
Toussaint et al. [18] developed a method to solve planning problems by
maximumlikelihood estimation. In this paper, we show how the hierarchy
discovery problem in partially observable domains can be tackled using a
similar maximum likelihood approach. Our technique rst transforms the problem
into a dynamic Bayesian network through which a hierarchical structure can
naturally be discovered while optimizing the policy. Experimental results
demonstrate that this approach scales better than previous techniques based on
non-convex optimization.
