This requires, for Goal-driven autonomy (GDA) is a reflective model example, experts to anticipate what discrepancies can occur, of goal reasoning that controls the focus of an identify what goals can be formulated, and define their agent's planning activities by dynamically relative priority. However, few techniques have been resolving unexpected discrepancies in the world investigated for learning this knowledge, and those that do state, which frequently arise when solving tasks in learn only goal formulation knowledge (Weber et al. 2010; complex environments. GDA agents have Powell et al. 2011). This can be problematic; while these performed well on such tasks by integrating agents may perform well in simple environments, in others a methods for discrepancy recognition, explanation, domain expert might not know the (state) expectations for goal formulation, and goal management. However, executing every action in every state, nor which goal should they require substantial domain knowledge, be pursued to resolve every possible discrepancy, or even including what constitutes a discrepancy and how the space of all possible discrepancies.