Case-Based Planning to Learn

J. William Murdock, Gordon Shippey, and Ashwin Ram

College of Computing
Georgia Institute of Technology
Atlanta, CA 30332-0280



Abstract. Learning can be viewed as a problem of planning a series of
modifications to memory. We adopt this view of learning and propose
the applicability of the case-based planning methodology to the task of
planning to learn. We argue that relatively simple, fine-grained primitive
inferential operators are needed to support flexible planning. We show
that it is possible to obtain the benefits of case-based reasoning within
a planning to learn framework.
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