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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.

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