Handling Planning Failures with Virtual Actions

Artificial intelligence (AI) planners have been widely used in many fields, such as intelligent agents, autonomous robots, web service compositions, etc. However, existing AI planners share a common problem: When given a problem to solve, they either return a solution if one exists or report that no solution is found. However, simply reporting failure leaves no clues for people to trace the causes of the planning failure. In this paper, we present a novel approach that can propose virtual actions in the event of planning failure. Virtual actions enable traditional planners to succeed and hence return an incomplete plan instead of merely an error message. More importantly, the specifications of the virtual actions suggest what the missing parts may contain, thus providing important clues to users as to the nature of the failure. Experimental results show that our approach constantly returns useful and comprehensible information for humans, thus making AI planning more practical when solving real-world problems.