This research examines a hybrid planner for a real-world mobile robot delivering messages in an office environment. The overall project, RUPART, uses "unified CBR" to combine behavior-based control with high-level case-based planning, using a single similarity metric to retrieve both behavior cases and plan cases. This paper focuses on case-based reasoning for behavior cases. Each behavior case describes a set of behaviors targeted for a particular environment. We explore features used to index behavior cases, as well as adaptation and learning methods. This project is incomplete at this time, and thorough evaluation, while underway, is not complete.
Published Date: May 2001
Registration: ISBN 978-1-57735-133-7
Copyright: Published by The AAAI Press, Menlo Park, California.