摘要翻译：
目前，犯罪人的特征是通过侦查人员或法医心理学家的解释，将犯罪现场特征和犯罪人的行为与犯罪人的特征和心理特征联系起来，从而获得犯罪人的特征和心理特征。本文采用概率网络(PN)建模方法，从大量的已知案例数据库中有效地、系统地发现变量之间不明显的、有价值的模式。PN结构可以用来提取行为模式，并洞察哪些因素影响这些行为。因此，当一个新的案件正在调查和轮廓变量未知，因为罪犯尚未确定，使用观察到的犯罪现场变量根据其在结构中的联系和相应的数值（概率）权值推断未知变量。其目的是产生一个更加系统和经验性的分析方法，并使用由此产生的PN模型作为决策工具。
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英文标题：
《Modeling of Human Criminal Behavior using Probabilistic Networks》
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作者：
Ramesh Kumar Gopala Pillai, Dr. Ramakanth Kumar .P
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最新提交年份：
2010
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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：
  Currently, criminals profile (CP) is obtained from investigators or forensic psychologists interpretation, linking crime scene characteristics and an offenders behavior to his or her characteristics and psychological profile. This paper seeks an efficient and systematic discovery of nonobvious and valuable patterns between variables from a large database of solved cases via a probabilistic network (PN) modeling approach. The PN structure can be used to extract behavioral patterns and to gain insight into what factors influence these behaviors. Thus, when a new case is being investigated and the profile variables are unknown because the offender has yet to be identified, the observed crime scene variables are used to infer the unknown variables based on their connections in the structure and the corresponding numerical (probabilistic) weights. The objective is to produce a more systematic and empirical approach to profiling, and to use the resulting PN model as a decision tool.
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PDF链接：
https://arxiv.org/pdf/1002.2202