Development of real planning and scheduling applications often requires the ability to handle uncertainty. Often this uncertainty is represented using probability information on the initial conditions and on the outcomes of actions. In this paper, we describe a novel probabilistic plan graph heuristic that computes information about the interaction between actions and between propositions. This information is used to find better relaxed plans and to compute their probability of success. This information guides a forward state space search for high probability, non-branching seed plans. These plans are then used in a planning and scheduling system that handles unexpected outcomes by runtime replanning. We briefly describe the heuristic, the search process, and the results on different domains from recent international planning competitions. We discuss the results of this study and some of the issues involved in advancing this work further.