摘要翻译：
我们讨论元认知建模作为认知建模和计算的增强。元认知控制机制应该使人工智能系统能够自我反思，对自己的行为进行推理，并适应新的情况。在这方面，我们提出了一个知识分类法和一个扩展的数据挖掘生命周期的实现细节，该生命周期支持已获得模型的实时集成。
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英文标题：
《On Introspection, Metacognitive Control and Augmented Data Mining Live
  Cycles》
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作者：
Daniel Sonntag
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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：
  We discuss metacognitive modelling as an enhancement to cognitive modelling and computing. Metacognitive control mechanisms should enable AI systems to self-reflect, reason about their actions, and to adapt to new situations. In this respect, we propose implementation details of a knowledge taxonomy and an augmented data mining life cycle which supports a live integration of obtained models.
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PDF链接：
https://arxiv.org/pdf/0807.4417