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A comparison of the rule and case-based reasoning approaches for the automation of help-desk operations at the tier-two level.

机译：比较规则和基于案例的推理方法以在第二层上实现帮助台操作的自动化。

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摘要

This exploratory study investigates the hypothesis that case-based reasoning (CBR) systems have advantages over rule-based reasoning (RBR) systems in providing automated support for Tier-2 help desk operations. The literature suggests that rule-based systems are best suited for problem solving when the system being analyzed is a single-purpose, specialized system and the rules for solving the problems are clear and do not change with high frequency. Case-based systems, because of their ability to offer alternative solutions for a given problem, give help-desk technicians more flexibility. Specifically, this dissertation aims to answer the following questions: (1) Which paradigm, rule-based or case-based reasoning, results in more precise solutions to problems when compared to the solutions derived from system manuals? (2) Which paradigm, rule-based or case-based reasoning, is more convenient to maintain in terms of knowledge modification (i.e. addition, deletion, or modification of rules/cases)? (3) Which paradigm, rule-based or case-based reasoning, enables help-desk technicians to solve problems in shorter time, and therefore at lower cost?;This is an exploratory study based on data collected from field experiments. RBR and CBR based prototypes were set up to support Tier-2 help desk operations. Trained help desk operators used the system to solve a set of benchmark problems. Data collected from this exercise was analyzed to answer the three research questions.;This exploratory study supported the hypothesis that the case-based paradigm is better suited for use in help desk environments at the Tier-2 level than is the rule-based paradigm. The case-based paradigm, because of its ability to offer alternative solutions for a given problem, gave the help-desk technician flexibility in applying a solution. Alternatively, the rule-based paradigm provided a solution if, and only if, a rule existed for a solution meeting the exact problem specifications. Further, in the absence of a rule, problem research time, using the rule-based paradigm, extended the time required to formulate a solution thereby increasing the cost.;This research provided sufficient information to show that the help-desk knowledge based system utilizing the case-based shell provided better overall solutions to problems than did the rule-based shell.
机译：这项探索性研究调查了以下假设：基于案例的推理（CBR）系统在为Tier-2服务台操作提供自动化支持方面优于基于规则的推理（RBR）系统。文献表明，当所分析的系统是一个单一用途的专用系统，并且解决问题的规则清晰且不会频繁更改时，基于规则的系统最适合解决问题。基于案例的系统由于能够为给定问题提供替代解决方案，因此可以使服务台技术人员更加灵活。具体而言，本论文旨在回答以下问题：（1）与从系统手册得出的解决方案相比，哪种范式（基于规则的推理或基于案例的推理）对问题的解决方案更为精确？ （2）在知识修改（即规则，案例的添加，删除或修改）方面，哪种范式（基于规则或案例推理）更易于维护？ （3）哪种范式（基于规则或案例的推理）使技术支持人员能够在更短的时间内解决问题，从而以更低的成本解决问题？；这是一项基于实地实验数据的探索性研究。建立了基于RBR和CBR的原型，以支持Tier-2服务台操作。受过训练的服务台操作员使用该系统解决了一系列基准问题。分析了从此练习中收集的数据以回答三个研究问题。该探索性研究支持以下假设：基于案例的范例比基于规则的范例更适合在Tier-2级别的服务台环境中使用。基于案例的范例由于能够为给定问题提供替代解决方案，因此使支持台技术人员可以灵活地应用解决方案。或者，基于规则的范式仅在存在满足确切问题规范的解决方案规则时才提供解决方案。此外，在没有规则的情况下，使用基于规则的范例进行问题研究的时间延长了制定解决方案所需的时间，从而增加了成本。该研究提供了足够的信息，表明基于帮助台知识的系统利用了与基于规则的外壳程序相比，基于案例的外壳程序可以更好地解决问题。

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  • 作者

    Bryant, Michael Forrester.;

  • 作者单位

    Nova Southeastern University.;

  • 授予单位 Nova Southeastern University.;
  • 学科 Information Science.;Artificial Intelligence.
  • 学位 Ph.D.
  • 年度 2009
  • 页码 217 p.
  • 总页数 217
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 信息与知识传播;人工智能理论;
  • 关键词

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