In hierarchical planning, selecting a plan at an abstract level affects planning performance because an abstract plan restricts the scope of primitive plans. However, if all primitive plans under the selected abstract plan have difficult-to-resolve conflicts with the plans of other agents, the final plan after conflict resolution will be inefficient or of low quality. In this paper, we propose a conflict estimation method to generate quality plans efficiently for multi-agent systems by appropriately selecting abstract plans in hierarchical planning. This method enables agents to learn which abstract plans are less likely to cause conflicts or which conflicts will be easy to resolve.