Planning for complex scenarios, particularly in which large teams of humans with distributed expertise and varying preferences share a set of resources, poses a number of challenges. While the team as a collective has full knowledge of the task requirements, constraints, and all existing preferences of individuals or subteams, no individual in the team knows the full model of the task and preferences. Such a scenario could be an ideal context to leverage an automated planning agent. However, in many complex domains, there exist context-dependent preferences and constraints that vary with each planning episode, so encoding a static model to represent the planning scenario is not possible