Abstract
The case adaptation process in case-based reasoning is often modeled as having two steps: enumerating differences between a new problem and the problem part of a retrieved case and then applying an adaptation rule for each difference. This model is sufficient when (1) predefined adaptation rules exist for all differences the system encounters, and (2) adaptation rules are sufficiently independent that interactions are not a major issue. This paper presents an approach to handling case adaptation when these assumptions fail. It proposes an approach, RObust ADaptation (ROAD), that uses heuristics to guide multi-step adaptations, with each adaptation chosen in the context of adaptations applied previously. To reduce the potential for accumulated degradation of solution quality from long adaptation chains, it performs incremental retrieval of new source cases along the adaptation path, resetting the partially modified case to the “ground truth” of existing cases when an existing case is nearby. An evaluation supports the benefits of the model and illuminates some tradeoffs.
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Notes
- 1.
In the experimental comparisons, when cases have been removed, all differences are statistically significant (\(p < .05\)), except for: Fig. 4(a) when more than 75 cases removed, Fig. 4 (b) when more than 25 have been removed, Fig. 5(b), which has no significant difference, and Fig. 6 (b), when fewer than 150 cases removed.
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Leake, D., Ye, X. (2019). On Combining Case Adaptation Rules. In: Bach, K., Marling, C. (eds) Case-Based Reasoning Research and Development. ICCBR 2019. Lecture Notes in Computer Science(), vol 11680. Springer, Cham. https://doi.org/10.1007/978-3-030-29249-2_14
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DOI: https://doi.org/10.1007/978-3-030-29249-2_14
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