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My Ph.D. dissertation (Calistri 1990) extends traditional methods of plan recognition to handle situations in which agents have flawed plans. This extension involves solving two problems: determining what sorts of mistakes people make when they reason about plans and figuring out how to recognize these mistakes when they occur. I have developed a complete classification of plan-based misconceptions, which categorizes all ways that a plan can fail, and I have developed a probabilistic interpretation of these misconceptions that can be used in principle to guide a bestfirst-search algorithm. I have also developed a program called Pathfinder that embodies a practical implementation of this theory. Pathfinder is a probability-based plan-recognition system based on the A* algorithm that uses information available from a user model to guide a bestfirst search through a plan hierarchy.