Towards Providing Explanations for AI Planner Decisions
  In order to engender trust in AI, humans must understand what an AI system is
trying to achieve, and why. To overcome this problem, the underlying AI process
must produce justifications and explanations that are both transparent and
comprehensible to the user. AI Planning is well placed to be able to address
this challenge. In this paper we present a methodology to provide initial
explanations for the decisions made by the planner. Explanations are created by
allowing the user to suggest alternative actions in plans and then compare the
resulting plans with the one found by the planner. The methodology is
implemented in the new XAI-Plan framework.
