Artificial intelligence search algorithms search discrete systems. To apply such algorithms to continuous systems, such systems must first be discretized, i.e. approximated as discrete systems. Action-based discretization requires that both action parameters and action timing be discretized. We focus on the problem of action timing discretization. After describing an epsilon-admissible variant of Korf’s recursive best-first search (epsilon-RBFS), we introduce iterative-refinement epsilon-admissible recursive best-first search (IR epsilon-RBFS) which offers significantly better performance for initial time delays between search states over several orders of magnitude. Lack of knowledge of a good time discretization is compensated for by knowledge of a suitable solution cost upper bound.