Plan synthesis in an open world is challenging

since some objects in an open world might be unknown

then we need to consider various scenarios before planning.One way to solve this problem is to employ sensors to observe unknown objects

assuming the sensors are capable of correctly capturing all information needed for planning.Different with previous work

we turn to the crowd for help before doing planning.We assume there are abundant annotators available to provide information needed before planning

however there is possibly a substantial amount of discrepancy from the crowd in practice.It is thus challenging to solve the planning problem with possibly noisy information provided by the crowd.We propose a novel approach with two phases.We first build a set of propositions with variables

and collect values from crowd for those propositions.We then estimate the actual values of variables and transform the problem in an open world into a normal planning problem and solve it.Finally

we empirically exhibit the effectiveness of our approach.