Evaluating the Effectiveness of Knowledge-Retrieval AIDS for Fault-Diagnosis Tasks

知識檢索輔助系統對於故障診斷作業績效之影響

10.29977/JCIIE.200409.0005

許尚華(Shang-Hwa Hsu)

案例式推理法(CBR) ； 知識基系統 ； 知識檢索 ； 故障診斷 ； Case-based reasoning(CBR) ； Knowledge-based system ； Knowledge retrieval

工業工程學刊

21卷5期（2004 / 09 / 01）

465 - 474

英文

知識檢索之輔助機制是決定人員能否有效地使用知識基系統之關鍵因素。本研究評估與比較案例式推理法（CBR）知識檢索輔助傳統之關鍵字知識檢索輔助系統在故障診斷作業之輔助績效。本研究之實驗情境有2（知識檢索輔助方式）X2（問題複雜度）等4種，其中知識檢索輔助式為受試者間變項，作業複雜度則為受試者內變項。由ANOVA分析結果顯示，CBR知識檢索輔助組之受試者所花的故障診斷時間顯著地低於關鍵字檢索輔助組。尤其，當問題複雜度提高時，CBR知識檢索輔助組之受試者之故障診斷績效更顯著地高於關鍵字檢索輔助組。由研究結果顯示，CBR知識檢索輔助能有效地增進故障診斷績效。因此，建議有關知識基系統之發展有必要將以演算推理為基礎之知識瀏覽與檢索機制列入考慮，以增進人員與系統間之互動效益。

The mechanism of knowledge retrieval is the key factor regarding whether users can efficiently apply a knowledge-based system. The effects case-based reasoning (CBR) retrieval and traditional key-term knowledge retrieval have on the performance of fault-diagnosis tasks were compared. The study was conducted using a split-plot factorial design: Two knowledge-retrieval aids by two problem complexities. Twenty subjects participated in the experiment and were randomly assigned to one of two groups. ANOVA results show that the fault-diagnosis times of the subjects in the CBR knowledge-retrieval group were significantly shorter than those of the key-term knowledge-retrieval group. In addition, the correct fault-diagnosis rates of the CBR knowledge-retrieval group. The differences were more significant when the diagnosis tasks were of higher degrees of complexity. The results demonstrate that the CBR retrieval aid can improve the performance of fault-diagnosis tasks. It is therefore suggested that algorithm-based knowledge browsing and retrieval aids should be included in the development of knowledge-based systems to support users in interacting with these systems.

1. Aamodt, A.,S. Wess,K. Althoff,M. Richter(1994).Topics in Case-based Reasoning.Explanation-driven Case-based Reasoning.
2. Abu-Hakima, S.,P. Brezillon(1995).Principles for the application of context in diagnostic problem solving.In Working Notes of the IJCAI-95 Workshop on Modelling Context in Knowledge Representation and Reasoning.
3. Cooke, N. J.(1994).Varieties of knowledge elicitation techniques.International Journal of Human-Computer Studies,41,801-849.
4. Dybala, T.,G. Tecuci,H. Rezazad(1996).The shared expertise model for teaching interactive design assistants.Engineering Applications in Artificial Intelligence,9,611-626.
5. Gaga, V.,V. Moustakis,Y. Vlachakis,G. Charissis(1996).IDD+: Enhancing medical knowledge acquisition using inductive machine learning.Applied Artificial Intelligence: An International Journal,10,79-94.
6. Kim, K.,W. G. Yoon,S. Choi(1999).Aiding fault diagnosis under symptom masking in dynamic systems.Ergonomics,42(11),1472-1481.
7. Kirk, R. E.(1995).Experimental Design: Procedures for the Behavioral Sciences.
8. Kolodner, J. L.(1989).Cased-Based Reasoning,13(4),507-549.
9. Kolonder, J. L.,R. L. Simpson(1989).The MEDIATOR: Analysis of an early case-based problem solver.Cognitive Science,13(4),507-549.
10. Leake, D. B.(1996).Case-Based Reasoning-Experiences, Lessons, Future Directions.
11. Maher, M. L.(1998).CBR as a framework for design.Proceedings of AAAI Workshop on Case-Based Reasoning Integrations.
12. Ohmann, C.,V. Moustakis,G. Yang,K. Lang(1996).Evaluation of automatic knowledge acquisition techniques in the diagnosis of acute abdominal pain.Artificial Intelligence in Medicine,8,23-36.
13. Sassen, J. M. Annemarie,Eric F. T. Buiël,Jan H. Hoegee(1994).A laboratory evaluation of a human operator support system.International Journal of Human-Computer Studies,40,895-931.
14. Schaafstal, A.,J. M. Schraagen(2000).Training of troubleshooting: A structured, task analytical approach.Cognitive Task Analysis.
15. Slade, S.(1991).Case-based reasoning for financial decision making.Proceedings of the First International Conference on Artificial Intelligence on Wall Street,232-237.
16. Turner, R.(1994).Adaptive Reasoning for Real-world Problems.London:Lawrence Erlbaum Associates.
17. Wang, S. L.,S. H. Hsu.A web-based CBR knowledge management system for PC troubleshooting.International Journal of Advanced Manufacturing Technology
18. Wang, S. L.,S. H. Hsu(2003).A hierarchical case knowledge acquisition method for troubleshooting.International Conference on Computer, Communication and Control Technologies: CCCT 2003 and the 9th. International Conference on Information Systems Analysis and Synthesis: ISAS 2003.
19. Wang, X. Z.(1999).Data Mining and Knowledge Discovery for Process Monitoring and Control.London:Springer.
20. Wilson, K. D.(1998).Using case-based reasoning to support health and safety compliance in the chemical industry.AI Magazine.

1. 葉怡玉、黃榮村、汪曼穎(2013)。台灣認知心理學的應用：從認知研究到科技脈絡裡的人性化設計。中華心理學刊，55(3)，381-404。