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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Case-Based Planning to Learn
by Bill Murdock, Gordon Shippey, Ashwin Ram
2nd International Conference on Case-Based Reasoning (ICCBR-97), Providence, RI, July 1997www.cc.gatech.edu/faculty/ashwin/papers/er-97-04.pdf