  Much recent research in decision theoretic planning has adopted Markov
decision processes (MDPs) as the model of choice, and has attempted to make
their solution more tractable by exploiting problem structure. One particular
algorithm, structured policy construction achieves this by means of a decision
theoretic analog of goal regression using action descriptions based on Bayesian
networks with tree-structured conditional probability tables. The algorithm as
presented is not able to deal with actions with correlated effects. We describe
a new decision theoretic regression operator that corrects this weakness. While
conceptually straightforward, this extension requires a somewhat more
complicated technical approach.
