It is traditional wisdom that one should start from the goals when generating a plan in order to focus the plan generation process on potentially relevant actions. The graphplan system, however, which is the most e cient planning system nowadays, builds a \planning graph" in a forward-chaining manner. Although this strategy seems to work well, it may possibly lead to problems if the planning task description contains irrelevant information. Although some irrelevant information can be ltered out by graphplan, most cases of irrelevance are not noticed. In this paper, we analyze the e ects arising from \irrelevant" information to planning task descriptions for di erent types of planners. Based on that, we propose a family of heuristics that select relevant information by minimizing the number of initial facts that are used when approximating a plan by backchaining from the goals ignoring any con icts. These heuristics, although not solution-preserving, turn out to be very useful...