摘要翻译：
自适应一直被认为是基于案例推理的致命弱点，因为它需要一些领域特定的知识，而这些知识很难获得。为了降低适应知识获取任务所带来的知识工程成本，本文将两种策略结合起来：利用知识发现技术从案例库中学习适应知识，并在问题解决时适时地触发适应知识获取会话。
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英文标题：
《Opportunistic Adaptation Knowledge Discovery》
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作者：
Fadi Badra (INRIA Lorraine - LORIA), Am\'elie Cordier (LIRIS), Jean
  Lieber (INRIA Lorraine - LORIA)
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最新提交年份：
2009
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
  Adaptation has long been considered as the Achilles' heel of case-based reasoning since it requires some domain-specific knowledge that is difficult to acquire. In this paper, two strategies are combined in order to reduce the knowledge engineering cost induced by the adaptation knowledge (CA) acquisition task: CA is learned from the case base by the means of knowledge discovery techniques, and the CA acquisition sessions are opportunistically triggered, i.e., at problem-solving time.
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PDF链接：
https://arxiv.org/pdf/0912.0132