DNA计算在基于规则系统错误检测中的应用

Behrouz Madahian, A. Salighehdar, R. Amini
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引用次数: 4

摘要

随着基于规则的系统(RBS)技术获得更广泛的接受,创建和维护大型知识库的需求将变得更加重要。演示没有错误的规则库仍然是采用该技术的障碍之一。在过去的几年中,在开发各种图形技术方面进行了大量的研究,例如利用Petri网来分析基于规则的系统中的结构错误,这些系统利用命题逻辑。基于规则的系统中的四个典型错误是冗余、循环、不完整和不一致。最近,人们提出了一种基于dna的计算方法来检测这些错误。本文提出了一种能够检测特殊情况下结构误差的算法。对于包含多个起始节点和目标节点的规则库,本文提出的算法不能正确去除结构误差,且算法缺乏通用性。在本研究中,主要基于Adleman操作的算法,能够检测规则库中可能出现的任何形式的结构错误。在运算时间复杂度为O(n*(Max{q, K, z}))的情况下,应用我们的算法的潜力是吉祥的,其中n是事实子句的数量;Q为最长推理链中的规则数;K是包含由不同数目的起始节点组成的先行项的管的数目;z表示由相同数目的起始节点组成的不同先行项的最大数目。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Applying DNA Computation to Error Detection Problem in Rule-Based Systems
As rule-based systems (RBS) technology gains wider acceptance, the need to create and maintain large knowledge bases will assume greater importance. Demonstrating a rule base to be free from error remains one of the obstacles to the adoption of this technology. In the past several years, a vast body of research has been carried out in developing various graphical techniques such as utilizing Petri Nets to analyze structural errors in rule-based systems, which utilize propositional logic. Four typical errors in rule-based systems are redundancy, circularity, incompleteness, and inconsistency. Recently, a DNA-based computing approach to detect these errors has been proposed. That paper presents algorithms which are able to detect structural errors just for special cases. For a rule base, which contains multiple starting nodes and goal nodes, structural errors are not removed correctly by utilizing the algorithms proposed in that paper and algorithms lack generality. In this study algorithms mainly based on Adleman’s operations, which are able to detect structural errors, in any form that they may arise in rule base, are presented. The potential of applying our algorithm is auspicious giving the operational time complexity of O(n*(Max{q, K, z})), in which n is the number of fact clauses; q is the number of rules in the longest inference chain; K is the number of tubes containing antecedents which are comprised of distinct number of starting nodes; and z denotes the maximum number of distinct antecedents comprised of the same number of starting nodes.
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