Abstract:Automated planning is able to handle increasingly complex applications, but can produce unsatisfactory results when the goal and metric provided in its model does not match the actual expectation and preference of those using the tool. This can be ameliorated by including methods for explainable planning (XAIP), to reveal the reasons for the automated planner's decisions and to provide more in-depth interaction with the planner. In this paper we describe at a highlevel two recent pieces of work in XAIP. First,… Show more
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