摘要翻译：
人类和人工系统都经常使用试错方法来解决问题。为了有效，这种类型的策略意味着有高质量的控制知识来指导寻求最优解。不幸的是，这种控制知识很少是完美的。此外，在人工系统中--就像在人类中一样--对自己的知识进行自我评估通常是困难的。然而，这种自我评估对于管理知识和确定何时修改知识是非常有用的。我们的工作目标是提出一种基于特定试错策略的自动评估人工系统控制知识质量的方法，即知情树搜索策略。我们的修正方法包括分析系统的执行日志，并使用信念理论来评估知识的全局质量。我们以实验的形式给出了一个在制图综合领域中使用这种方法的实际工业应用。到目前为止，使用我们的方法的结果令人鼓舞。
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英文标题：
《Using Belief Theory to Diagnose Control Knowledge Quality. Application
  to cartographic generalisation》
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作者：
Patrick Taillandier (COGIT, UMMISCO), C\'ecile Duch\^ene (COGIT),
  Alexis Drogoul (UMMISCO, MSI)
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最新提交年份：
2012
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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：
  Both humans and artificial systems frequently use trial and error methods to problem solving. In order to be effective, this type of strategy implies having high quality control knowledge to guide the quest for the optimal solution. Unfortunately, this control knowledge is rarely perfect. Moreover, in artificial systems-as in humans-self-evaluation of one's own knowledge is often difficult. Yet, this self-evaluation can be very useful to manage knowledge and to determine when to revise it. The objective of our work is to propose an automated approach to evaluate the quality of control knowledge in artificial systems based on a specific trial and error strategy, namely the informed tree search strategy. Our revision approach consists in analysing the system's execution logs, and in using the belief theory to evaluate the global quality of the knowledge. We present a real-world industrial application in the form of an experiment using this approach in the domain of cartographic generalisation. Thus far, the results of using our approach have been encouraging.
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PDF链接：
https://arxiv.org/pdf/1204.4989