寻找兼容性
Looking for plausibility
摘要：
在解释实验数据时，人们实际上是在寻找合理的解释。我们寻找一种可解释性的度量，我们可以用它来比较不同的可能解释，并且当有不同的数据集时，可以将其结合起来。这与传统的概率度量以及建议的可能性度量形成对比。我们定义了这种可扩展性度量应该具有的特征。在了解该度量的概念时，我们探讨了似然性与溯因推理以及与贝叶斯概率的关系。我们还比较了Dempster-Schaefer证据理论，它也有自己的定义的可验证性。溯因可以与推理规则中的双条件性相关联，这提供了一个与柯林斯-米哈尔斯基可解释性理论相关的平台。最后，使用接线逻辑到Hopfield神经网络的形式主义，我们问，如果这是相关的，在获得这一措施。
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.
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