遠端診斷與除錯系統的複合式知識儲存與擷取模型

A Hybrid Knowledge Storage and Retrieving Model for Remote Diagnosis and Troubleshooting System

客戶服務的品質已成為現代企業維持競爭優勢的一個重要因素。遠端系統診斷 ( Remote Diagnosis ) 與除錯 (Troubleshooting ) 的功能將可適時用來協助與強化自動化客戶服務的機能，而此功能的發揮有賴於後端密集知識的支援。本研究利用貝氏認知網路並結合案例式認知模型來建置一個客戶端的產品維修診斷系統，將企業後端許多具不確定性因素的知識作結構性的儲存並能有效的透過簡易的介面提供給前端使用者來加以使用。在本文中我們說明了整合上述模型的方法與原因，並實作一案例以驗證本模式的可行性。此案例亦可作為未來企業應用時的參考。

The quality of the customer service has become a point of competitive distinction and positional advantage. The functionality of remote system diagnosis and troubleshooting can increase the quality of customer service. However, to provide such a new functionality, business must possess a highly concentrated backstage support capability. In this study we develop an intelligent self customer trouble-shooting assistant system using a hybrid method which integrates Bayesian Belief Network (BBN) and Case-based Reasoning model for customer problems solving. The proposed method performs probability inference to model uncertain domain knowledge and allows unique experience to be memorized and retrieved in a more easy way. The techniques described are demonstrated by an example developed in our laboratory. The example can be a reference model for the people who are interesting to develop a self customer service system.