  In a deterministic world, a planning agent can be certain of the consequences
of its planned sequence of actions. Not so, however, in dynamic, stochastic
domains where Markov decision processes are commonly used. Unfortunately these
suffer from the curse of dimensionality: if the state space is a Cartesian
product of many small sets (dimensions), planning is exponential in the number
of those dimensions.
  Our new technique exploits the intuitive strategy of selectively ignoring
various dimensions in different parts of the state space. The resulting
non-uniformity has strong implications, since the approximation is no longer
Markovian, requiring the use of a modified planner. We also use a spatial and
temporal proximity measure, which responds to continued planning as well as
movement of the agent through the state space, to dynamically adapt the
abstraction as planning progresses.
  We present qualitative and quantitative results across a range of
experimental domains showing that an agent exploiting this novel approximation
method successfully finds solutions to the planning problem using much less
than the full state space. We assess and analyse the features of domains which
our method can exploit.
