A Novel Approach for Treating Uncertain Rule-based Knowledge Conflicts

Rule base have traditionally emphasized the verification of structural errors in the rule base. For conflicting or redundant rules, designated rules are usually followed to implement prioritized or direct deletions. However, there exist no proper methods by which to resolve conflicting or redundant rules. Due to the uncertainty of uncertain knowledge itself, it is difficult to treat conflicting rules, and the citation of erroneous knowledge leads to mistakes in decision making. Among users, 94% report perplexity when conflicting or redundant rules are cited. It is therefore a necessity to confirm the existence and reliability of the cited knowledge. The current study attempts to provide an uncertain rule-based knowledge conflict treatment algorithm by integrating a group decision and an uncertain inference. In the algorithm, a “reliability factor” refers to the reliability level of the conflicting or redundant rules, while the “certainty factor” indicates the existence of the knowledge itself. A “certainty reliability index” is used to show both the existence of the knowledge itself and its reliability. For conflicting or redundant rules, it is suggested that the knowledge with a higher reliability factor be chosen. Among users, 92% reported that the algorithm is helpful to knowledge application and an aid to the decision-making process.