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首页> 外文会议>International conference on case-based reasoning >Exploration vs. Exploitation in Case-Base Maintenance: Leveraging Competence-Based Deletion with Ghost Cases
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Exploration vs. Exploitation in Case-Base Maintenance: Leveraging Competence-Based Deletion with Ghost Cases

机译：基于案例的维护中的探索与开发：利用基于能力的删除与Ghost案例

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Case-base maintenance research has extensively studied strategies for competence-retaining case base compression. Such approaches generally rely on the representativeness assumption that current case base contents can be used as a proxy for future problems when determining cases to retain. For mature case bases in stable domains, this assumption works well. However, representativeness may not hold for sparse case bases during initial case base growth, for dynamically changing domains, or when a case base built for one task is applied to cross-domain problem-solving in another. This paper presents a new method for competence-preserving deletion, Expansion-Contraction Compression (ECC), aimed at improving competence preservation when the representativeness assumption is only partially satisfied. ECC precedes compression with adaptation-based exploration of previously unseen parts of the problem space to create "ghost cases" and exploits them to broaden the range of cases available for competence-based deletion. Experimental results support that this method increases competence and quality retention for less representative case bases. They also reveal the unexpected result that ECC can improve retention of competence and quality even for representative case bases.
机译：基于案例的维护研究已经广泛研究了用于保持能力的基于案例的压缩策略。此类方法通常依赖于代表性假设，即当确定要保留的案例时，当前案例库的内容可以用作未来问题的代理。对于稳定域中的成熟案例库，此假设效果很好。但是，在初始案例库增长期间，动态更改域或将为一项任务构建的案例库应用于另一项任务的跨域问题解决时，稀疏案例库的代表性可能不成立。本文提出了一种保留胜任力的新方法，即扩展收缩压缩（ECC），旨在在仅部分满足代表性假设的情况下提高胜任力的保留。 ECC先进行压缩，然后对问题空间中以前看不见的部分进行基于适应的探索，以创建“鬼案”，并利用它们扩大可用于基于胜任力删除的案例范围。实验结果支持这种方法可以提高代表性较低的案例的能力和质量保留率。他们还揭示了ECC可以改善保留能力和质量的意外结果，即使对于有代表性的案例库也是如此。

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