摘要翻译：
在解释实验数据时，人们实际上是在寻找看似合理的解释。我们寻找一种似是而非的度量，用它我们可以比较不同的可能解释，当有不同的数据集时，可以将其结合起来。这与传统的概率测度和提议的可能性测度形成了对比。我们定义了这种合理性度量应该具有哪些特征。在得到这个测度的概念时，我们探讨了合情性与溯因推理以及与贝叶斯概率的关系。我们还与Dempster-Schaefer证据理论进行了比较，后者也有自己的似然性定义。在推理规则中，外展可以与双condonitability相联系，这提供了一个与柯林斯-米哈尔斯基似然性理论相联系的平台。最后，使用一种将逻辑连接到Hopfield神经网络上的形式，我们询问这是否与获得这个测度相关。
---
英文标题：
《Looking for plausibility》
---
作者：
Wan Ahmad Tajuddin Wan Abdullah
---
最新提交年份：
2010
---
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：
  In the interpretation of experimental data, one is actually looking for plausible explanations. We look for a measure of plausibility, with which we can compare different possible explanations, and which can be combined when there are different sets of data. This is contrasted to the conventional measure for probabilities as well as to the proposed measure of possibilities. We define what characteristics this measure of plausibility should have.   In getting to the conception of this measure, we explore the relation of plausibility to abductive reasoning, and to Bayesian probabilities. We also compare with the Dempster-Schaefer theory of evidence, which also has its own definition for plausibility. Abduction can be associated with biconditionality in inference rules, and this provides a platform to relate to the Collins-Michalski theory of plausibility. Finally, using a formalism for wiring logic onto Hopfield neural networks, we ask if this is relevant in obtaining this measure.
---
PDF链接：
https://arxiv.org/pdf/1012.5705