摘要翻译：
许多现实世界的问题可以表示为优化问题。解决这类问题意味着在所有可能的解中找到使一个评价函数最大化的解。解决这类问题的一种方法是使用知情搜索策略。这种策略的原则是使用问题本身定义之外的特定问题知识，比不知情的策略更有效地找到解决方案。这种策略要求定义特定于问题的知识（启发式）。基于信息技术的系统的效率和效能直接取决于所使用的知识质量。不幸的是，获取和保持这样的知识可能是挑剔的。本文的目的是提出一种基于知情树搜索策略的系统知识自动修正方法。我们的方法包括分析系统执行日志，并根据这些日志修改知识，将修改问题建模为知识空间探索问题。我们给出了一个我们在一个应用领域中进行的实验，这个应用领域经常使用信息搜索策略：制图综合。
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英文标题：
《Knowledge revision in systems based on an informed tree search strategy
  : 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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一级分类：Computer Science        计算机科学
二级分类：Machine Learning        机器学习
分类描述：Papers on all aspects of machine learning research (supervised, unsupervised, reinforcement learning, bandit problems, and so on) including also robustness, explanation, fairness, and methodology. cs.LG is also an appropriate primary category for applications of machine learning methods.
关于机器学习研究的所有方面的论文（有监督的，无监督的，强化学习，强盗问题，等等），包括健壮性，解释性，公平性和方法论。对于机器学习方法的应用，CS.LG也是一个合适的主要类别。
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英文摘要：
  Many real world problems can be expressed as optimisation problems. Solving this kind of problems means to find, among all possible solutions, the one that maximises an evaluation function. One approach to solve this kind of problem is to use an informed search strategy. The principle of this kind of strategy is to use problem-specific knowledge beyond the definition of the problem itself to find solutions more efficiently than with an uninformed strategy. This kind of strategy demands to define problem-specific knowledge (heuristics). The efficiency and the effectiveness of systems based on it directly depend on the used knowledge quality. Unfortunately, acquiring and maintaining such knowledge can be fastidious. The objective of the work presented in this paper is to propose an automatic knowledge revision approach for systems based on an informed tree search strategy. Our approach consists in analysing the system execution logs and revising knowledge based on these logs by modelling the revision problem as a knowledge space exploration problem. We present an experiment we carried out in an application domain where informed search strategies are often used: cartographic generalisation.
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PDF链接：
https://arxiv.org/pdf/1204.4991