摘要翻译：
研究了基本事件上有条件约束的概率推演问题。我们证明了基本事件上具有条件约束的全局完全概率推演是NP-hard的。然后我们集中讨论了条件约束树中概率推演的特例。我们阐述了非常有效的技术，用于全局完全概率推导。具体而言，对于具有点概率的条件约束树，我们提出了一种全局完全概率推理的局部方法，该方法以条件约束树的大小为线性时间运行。对于具有区间概率的条件约束树，我们证明了通过求解非线性规划，可以用全局方法进行全局完全的概率推导。我们给出了如何将这些非线性规划转化为等价的线性规划，这些线性规划可以在多项式时间内以条件约束树的大小求解。
---
英文标题：
《Probabilistic Deduction with Conditional Constraints over Basic Events》
---
作者：
T. Lukasiewicz
---
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 study the problem of probabilistic deduction with conditional constraints over basic events. We show that globally complete probabilistic deduction with conditional constraints over basic events is NP-hard. We then concentrate on the special case of probabilistic deduction in conditional constraint trees. We elaborate very efficient techniques for globally complete probabilistic deduction. In detail, for conditional constraint trees with point probabilities, we present a local approach to globally complete probabilistic deduction, which runs in linear time in the size of the conditional constraint trees. For conditional constraint trees with interval probabilities, we show that globally complete probabilistic deduction can be done in a global approach by solving nonlinear programs. We show how these nonlinear programs can be transformed into equivalent linear programs, which are solvable in polynomial time in the size of the conditional constraint trees.
---
PDF链接：
https://arxiv.org/pdf/1105.5461