Acquiring Planning Knowledge via Crowdsourcing

Introduction Plan synthesis often requires complete domain models and initial states as input. In many real world applications, it is difficult to build domain models and provide complete initial state beforehand. In this paper we propose to turn to the crowd for help before planning. We assume there are annotators available to provide information needed for building domain models and initial states. However, there might be a substantial amount of discrepancy within the inputs from the crowd. It is thus challenging to address the planning problem with possibly noisy information provided by the crowd. We address the problem by two phases. We first build a set of Human Intelligence Tasks (HITs), and collect values from the crowd. We then estimate the actual values of variables and feed the values to a planner to solve the problem. In contrast to previous efforts (Zhang et al. 2012; Manikonda et al. 2014) that ask the crowd to do planning, we exploit knowledge about initial states and/or action models from the crowd and feed the knowledge to planners to do planning. We call our approach PAN-CROWD, stands for Planning by Acquiring kNowledge from the CROWD.