A self-improving helpdesk service system using case-based reasoning techniques

被引:25
作者
Chang, KH
Raman, R
Carlisle, WH
Cross, JH
机构
[1] Dept. of Comp. Sci. and Engineering, Auburn University, 107 Dunstan Hall, Auburn
关键词
case-based reasoning; machine learning; expert system; artificial intelligence; helpdesk service automation;
D O I
10.1016/0166-3615(96)00033-4
中图分类号
TP39 [计算机的应用];
学科分类号
081203 [计算机应用技术]; 0835 [软件工程];
摘要
Case-Based Reasoning (CBR) is the process of solving a given problem based on the knowledge gained from solving precedents. It is an effective technique in the area of customer services or helpdesks. That is, a CBR system is used to solve most of the commonly occurring customer problems. While the implementation techniques may vary, most CBR systems include the following five steps: case representation and storage, precedent matching and retrieval, adaptation of the retrieved solution, validation of the solution, and finally, casebase update to include the information gained from the new problem. This paper details the various implementation techniques for these five steps, while focusing on a particular helpdesk system, namely SmartUSA, developed for the Union Camp Corporation. This system solves a customer's problem by filtering the problem description through an alias table to generate a brief description and then matching the brief description with the cases in the database. It has proved to be an effective and user-friendly system that has successfully handled different descriptions of the same problem and allowed for the casebase to be built in free-format (plain) text. This system has significantly reduced the workload and the response time in the customer services department of the Union Camp Corporation.
引用
收藏
页码:113 / 125
页数:13
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