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A novel belief rule base representation, generation and its inference methodology

机译：一种新颖的信念规则库表示，生成及其推理方法

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摘要

Advancement and application of rule-based systems have always been a key research area in computer-aided support for human decision making due to the fact that rule base is one of the most common frameworks for expressing various types of human knowledge in an intelligent system. In this paper, a novel rule-based representation scheme with a belief structure is proposed firstly along with its inference methodology. Such a rule base is designed with belief degrees embedded in the consequent terms as well as in the all antecedent terms of each rule, which is shown to be capable of capturing vagueness, incompleteness, uncertainty, and nonlinear causal relationships in an integrated way. The overall representation and inference framework offers a further improvement and great extension of the recently developed belief Rule base Inference Methodology (refer to as RIMER), although they still share a common scheme at the final step of inference, i.e., the evidential reasoning (ER) approach is applied to the rule combination. It is worth noting that this new extended belief rule base representation is a great extension of traditional rule base as well as fuzzy rule base by encompassing the uncertainty description in the rule antecedent and consequent. Subsequently, a simple but efficient and powerful method for automatically generating such extended belief rule base from numerical data is proposed involving neither time-consuming iterative learning procedure nor complicated rule generation mechanisms but keeping the relatively good performance, which thanks to the new features of the extended rule base with belief structures. Then some case studies in oil pipeline leak detection and software defect detection are provided to illustrate the proposed new rule base representation, generation, and inference procedure as well as demonstrate its high performance and efficiency by comparing with some existing approaches.
机译：由于规则库是在智能系统中表达各种类型人类知识的最常见框架之一，因此基于规则的系统的发展和应用一直是计算机辅助人类决策支持的关键研究领域。本文首先提出了一种新的基于规则的信念结构表示方案及其推理方法。设计这样的规则库时，在每个术语的后续术语以及所有先前术语中都嵌入了置信度，这表明可以综合地捕获模糊性，不完全性，不确定性和非线性因果关系。总体表示和推理框架对最近开发的信念规则库推理方法（称为RIMER）进行了进一步的改进和扩展，尽管它们在推理的最后步骤仍共享一个通用方案，即证据推理（ER）。 ）方法应用于规则组合。值得注意的是，这种新的扩展的信念规则库表示形式将传统的规则库以及模糊规则库都进行了很大的扩展，将不确定性描述包含在规则的前因和后因中。随后，提出了一种简单而有效的强大方法，该方法可以从数值数据中自动生成这种扩展的置信规则库，该方法既不耗时的迭代学习过程，也不需要复杂的规则生成机制，而是保持相对较好的性能，这要归功于该算法的新功能。具有信念结构的扩展规则库。然后，通过对石油管道泄漏检测和软件缺陷检测的一些案例研究，来说明所提出的新规则库表示，生成和推理过程，并通过与一些现有方法进行比较来证明其高性能和高效率。

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