School of Computer Science, Carnegie Mellon University
To appear in Agents '98
Our system collects execution traces, and extracts relevant information to improve the efficiency of generated plans. In this article, we present the representation of the path planner and the navigation modules, and describe the execution trace. We show how training data is extracted from the execution trace.
We introduce the concept of situation-dependent costs, where situational features can be attached to the costs used by the path planner. In this way, the planner can generate paths that are appropriate for a given situation. We present experimental results from a simulated, controlled environment as well as from data collected from the actual robot.