摘要翻译：
我们表明，估计固定规模约束满足问题(CSP)实例的复杂性（均值和分布）可能是非常困难的。我们讨论了该问题的两个主要方面：定义复杂性度量和生成随机无偏实例。对于第一个问题，我们依赖于一个通用框架和我们在CISSE08上提出的复杂性度量。对于生成问题，我们将我们的分析局限于数独示例，我们提供了一个解决方案，也解释了为什么它如此困难。
---
英文标题：
《Unbiased Statistics of a CSP - A Controlled-Bias Generator》
---
作者：
Denis Berthier (DSI)
---
最新提交年份：
2011
---
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。
--

---
英文摘要：
  We show that estimating the complexity (mean and distribution) of the instances of a fixed size Constraint Satisfaction Problem (CSP) can be very hard. We deal with the main two aspects of the problem: defining a measure of complexity and generating random unbiased instances. For the first problem, we rely on a general framework and a measure of complexity we presented at CISSE08. For the generation problem, we restrict our analysis to the Sudoku example and we provide a solution that also explains why it is so difficult.
---
PDF链接：
https://arxiv.org/pdf/1111.4083