A validation-structure-based theory of plan modification and reuse
Artificial Intelligence
De´ja` Vu: a hierarchical case-based reasoning system for software design
ECAI '92 Proceedings of the 10th European conference on Artificial intelligence
Automatically generating abstractions for planning
Artificial Intelligence
Downward refinement and the efficiency of hierarchical problem solving
Artificial Intelligence
Sorting by reversals is difficult
RECOMB '97 Proceedings of the first annual international conference on Computational molecular biology
Adaptation-guided retrieval: questioning the similarity assumption in reasoning
Artificial Intelligence
On the Role of Abstraction in Case-Based Reasoning
EWCBR '96 Proceedings of the Third European Workshop on Advances in Case-Based Reasoning
Stratified Case-Based Reasoning in Non-Refinable Abstraction Hierarchies
ICCBR '97 Proceedings of the Second International Conference on Case-Based Reasoning Research and Development
Building and refining abstract planning cases by change of representation language
Journal of Artificial Intelligence Research
A domain-independent algorithm for plan adaptation
Journal of Artificial Intelligence Research
Stratified case-based reasoning: reusing hierarchical problem solving episodes
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
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Stratified case-based reasoning (SCBR) is a technique in which case abstractions are used to assist case retrieval, matching, and adaptation. Previous work has shown that SCBR can significantly decrease the computational expense required for retrieval, matching, and adaptation under a variety of different problem conditions. This paper extends this work to two new domains: a problem in combinatorial optimization, sorting by prefix reversal; and logistics planning. An empirical evaluation in the prefix-reversal problem showed that SCBR reduced search cost, but severely degraded solution quality. By contrast, in logistics planning, use of SCBR as an indexing mechanism led to faster solution times and permitted more problems to be solved than either hierarchical problem solving (by ALPINE) or ground level CBR (by SPA) alone. The primary factor responsible for the difference in SCBR's performance in these two domains appeared to be that the optimal-case utility was low in the prefix-reversal task but high in logistics planning.