Classifying and Detecting Plan-Based Misconceptions for Robust Plan Recognition

Randall J. Calistri-Yeh

Abstract


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 best-first 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.

Full Text:

PDF


DOI: http://dx.doi.org/10.1609/aimag.v12i3.911

Copyright © 2014, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.