Integrating Planning and Execution in Stochastic Domains
  We investigate planning in time-critical domains represented as Markov
Decision Processes, showing that search based techniques can be a very powerful
method for finding close to optimal plans. To reduce the computational cost of
planning in these domains, we execute actions as we construct the plan, and
sacrifice optimality by searching to a fixed depth and using a heuristic
function to estimate the value of states. Although this paper concentrates on
the search algorithm, we also discuss ways of constructing heuristic functions
suitable for this approach. Our results show that by interleaving search and
execution, close to optimal policies can be found without the computational
requirements of other approaches.
