  Replanners are efficient methods for solving non-deterministic planning
problems. Despite showing good scalability, existing replanners often fail to
solve problems involving a large number of misleading plans, i.e., weak plans
that do not lead to strong solutions, however, due to their minimal lengths,
are likely to be found at every replanning iteration. The poor performance of
replanners in such problems is due to their all-outcome determinization. That
is, when compiling from non-deterministic to classical, they include all
compiled classical operators in a single deterministic domain which leads
replanners to continually generate misleading plans. We introduce an offline
replanner, called Safe-Planner (SP), that relies on a single-outcome
determinization to compile a non-deterministic domain to a set of classical
domains, and ordering heuristics for ranking the obtained classical domains.
The proposed single-outcome determinization and the heuristics allow for
alternating between different classical domains. We show experimentally that
this approach can allow SP to avoid generating misleading plans but to generate
weak plans that directly lead to strong solutions. The experiments show that SP
outperforms state-of-the-art non-deterministic solvers by solving a broader
range of problems. We also validate the practical utility of SP in real-world
non-deterministic robotic tasks.
