Learning Situation-Dependent Rules:
Karen Zita Haigh and Manuela M. Veloso,
Improving Planning from
School of Computer Science, Carnegie Mellon University
Submitted to AAAI '98
Real world robot tasks are so complex that it is hard to hand-tune all of the
domain knowledge, especially to model the dynamics of the environment. In this
paper we present techniques for learning from real-world
robot execution to improve planning and execution performance. We present our
work learning from execution to improve a task planner's performance.
Our system collects execution traces from the robot, and automatically extracts
relevant information to improve the efficiency of generated plans. We
introduce the concept of situation-dependent rules, where situational
features are used to identify the conditions that affect action achievability.
It converts this execution knowledge into a sybmolic representation that the
planner can use to generate plans appropriate for given situations.