Many large Markov decision processes (MDPs) can be represented compactly
using a structured representation such as a dynamic Bayesian network.
Unfortunately, the compact representation does not help standard MDP
algorithms, because the value function for the MDP does not retain the
structure of the process description. We argue that in many such MDPs,
structure is approximately retained. That is, the value functions are
nearly *additive*: closely approximated by a linear function over
factors associated with small subsets of problem features. Based on this
idea, we present a convergent approximate value determination algorithm for
structured MDPs. The algorithm maintains an additive value function,
alternating dynamic programming steps with steps that project the result
back into the restricted space of additive functions. We show that both
the dynamic programming and the projection steps can be computed efficiently,
despite the fact that the number of states is exponential in the number of
state variables.