Most current planners assume complete domain models and focus on generating correct plans. Unfortunately, domain modeling is a laborious and error-prone task, thus real world agents have to plan with incomplete domain models. While domain experts cannot guarantee completeness, often they are able to circumscribe the incompleteness of the model by providing annotations as to which parts of the domain model may be incomplete. In this paper, we study planning problems with incomplete STRIPS domain models where the annotations specify possible preconditions and effects of actions. We show that the problem of assessing the quality of a plan, or its plan robustness, is #P-complete, establishing its equivalence with the weighted model counting problems. We introduce two approximations, lower and upper bound, for plan robustness, and then utilize them to derive heuristics for synthesizing robust plans. Our planning system, PISA, incorporating stochastic local search with these novel techniques outperforms a state-of-the-art planner handling incomplete domains in most of the tested domains, both in terms of plan quality and planning time.