Title :
Whither AI in test and diagnosis?
Author :
Fenton, Billy ; McGinnity, T.M. ; Maguire, L.P.
Author_Institution :
Ulster Univ., UK
Abstract :
In an increasingly competitive marketplace system complexity continues to grow, but time-to-market and lifecycle are reducing. This has driven the need for automated test and diagnostic tools. As test and diagnosis is a high-level human activity, AI-based solutions have been pursued. This has been an active research area for some decades, but the industrial acceptance of AI approaches, particularly in cost-sensitive areas, has not been high. This paper reviews the history and current state of AI in test and diagnosis, discusses future challenges, and introduces the authors´ work in addressing some of these challenges in a diagnostic context
Keywords :
automatic test equipment; automatic test software; case-based reasoning; diagnostic expert systems; explanation; fault diagnosis; fuzzy logic; history; knowledge acquisition; learning (artificial intelligence); model-based reasoning; neural nets; reviews; AI-ESTATE; Al-based solutions; automated test and diagnostic tools; case-based reasoning; diagnostic inference models; evolutionary computation; explanation; fault diagnosis; future challenges; fuzzy logic; industrial acceptance; machine learning; neural networks; rule-based reasoning; system complexity; Artificial intelligence; Artificial neural networks; Automatic testing; Costs; Diagnostic expert systems; Fault diagnosis; Fuzzy logic; History; Machine learning; Telephony;
Conference_Titel :
AUTOTESTCON Proceedings, 2001. IEEE Systems Readiness Technology Conference
Conference_Location :
Valley Forge, PA
Print_ISBN :
0-7803-7094-5
DOI :
10.1109/AUTEST.2001.948979
