This research will investigate the usefulness of applying heuristic search algorithms, an important class of artificial intelligence problem solving techniques, to solve various types of Markov decision processes. The aim of the research is to enhance the tractability of these models of sequential decision making under uncertainty. It is anticipated that the research will produce needed extensions of a wide class of heuristic search algorithms as well as suggesting potential areas for integrating concepts in heuristic search with concepts associated with stochastic control and the decision sciences.
