School of Computer Science, Carnegie Mellon University
Final version; To appear in AIPS '98 (AI Planning Systems)
We present a general approach for learning situation-dependent rules from execution, which correlates environmental features with learning opportunities, thereby detecting patterns and allowing planners to predict and avoid failures. We present two implementations of the general learning approach, in the robot's path planner, and in the task planner. We present empirical data to show the effectivness of ROGUE's novel learning approach.
This paper is essentially a summary of my thesis.